Facies classification using Machine Learning

Introduction

Author: Anjum Sayed. My prediction approach is as follows:

"Upsampling" the log data to a more conventional sample rate
Testing different classifiers:
1. SVM (using GridSearchCV to find best parameters)
2. DecisionTree (using GridSearchCV to find best parameters)
3. XGBoost
4. Naive Bayes
5. AdaBoost
6. RandomForest
7. Nearest neighbour (using GridSearchCV to find best parameters)
8. DNN TensorFlow classifier
9. LSTM TensorFlow classifier (TODO)
Taking the 5 best classifiers and creating an ensemble majority vote classifier

There are many ways to improve on my method. See the future work at the end section for ideas.

Exploring the dataset

First, we will examine the data set we will use to train the classifier. The training data is contained in the file facies_vectors.csv. The dataset consists of 5 wireline log measurements, two indicator variables and a facies label at half foot intervals. In machine learning terminology, each log measurement is a feature vector that maps a set of 'features' (the log measurements) to a class (the facies type). We will use the pandas library to load the data into a dataframe, which provides a convenient data structure to work with well log data.



In [1]:

    
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable

from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment = None

filename = 'facies_vectors.csv'
training_data = pd.read_csv(filename)
training_data









    Out[1]:






  
    
      
      Facies
      Formation
      Well Name
      Depth
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
    
  
  
    
      0
      3
      A1 SH
      SHRIMPLIN
      2793.0
      77.450
      0.664
      9.900
      11.915
      4.600
      1
      1.000
    
    
      1
      3
      A1 SH
      SHRIMPLIN
      2793.5
      78.260
      0.661
      14.200
      12.565
      4.100
      1
      0.979
    
    
      2
      3
      A1 SH
      SHRIMPLIN
      2794.0
      79.050
      0.658
      14.800
      13.050
      3.600
      1
      0.957
    
    
      3
      3
      A1 SH
      SHRIMPLIN
      2794.5
      86.100
      0.655
      13.900
      13.115
      3.500
      1
      0.936
    
    
      4
      3
      A1 SH
      SHRIMPLIN
      2795.0
      74.580
      0.647
      13.500
      13.300
      3.400
      1
      0.915
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      4144
      5
      C LM
      CHURCHMAN BIBLE
      3120.5
      46.719
      0.947
      1.828
      7.254
      3.617
      2
      0.685
    
    
      4145
      5
      C LM
      CHURCHMAN BIBLE
      3121.0
      44.563
      0.953
      2.241
      8.013
      3.344
      2
      0.677
    
    
      4146
      5
      C LM
      CHURCHMAN BIBLE
      3121.5
      49.719
      0.964
      2.925
      8.013
      3.190
      2
      0.669
    
    
      4147
      5
      C LM
      CHURCHMAN BIBLE
      3122.0
      51.469
      0.965
      3.083
      7.708
      3.152
      2
      0.661
    
    
      4148
      5
      C LM
      CHURCHMAN BIBLE
      3122.5
      50.031
      0.970
      2.609
      6.668
      3.295
      2
      0.653
    
  

4149 rows × 11 columns

This data is from the Council Grove gas reservoir in Southwest Kansas. The Panoma Council Grove Field is predominantly a carbonate gas reservoir encompassing 2700 square miles in Southwestern Kansas. This dataset is from nine wells (with 4149 examples), consisting of a set of seven predictor variables and a rock facies (class) for each example vector and validation (test) data (830 examples from two wells) having the same seven predictor variables in the feature vector. Facies are based on examination of cores from nine wells taken vertically at half-foot intervals. Predictor variables include five from wireline log measurements and two geologic constraining variables that are derived from geologic knowledge. These are essentially continuous variables sampled at a half-foot sample rate.

The seven predictor variables are:

Five wire line log curves include gamma ray (GR), resistivity logging (ILD_log10), photoelectric effect (PE), neutron-density porosity difference and average neutron-density porosity (DeltaPHI and PHIND). Note, some wells do not have PE.
Two geologic constraining variables: nonmarine-marine indicator (NM_M) and relative position (RELPOS)

The nine discrete facies (classes of rocks) are:

Nonmarine sandstone
Nonmarine coarse siltstone
Nonmarine fine siltstone
Marine siltstone and shale
Mudstone (limestone)
Wackestone (limestone)
Dolomite
Packstone-grainstone (limestone)
Phylloid-algal bafflestone (limestone)

These facies aren't discrete, and gradually blend into one another. Some have neighboring facies that are rather close. Mislabeling within these neighboring facies can be expected to occur. The following table lists the facies, their abbreviated labels and their approximate neighbors.

Facies	Label	Adjacent Facies
1	SS	2
2	CSiS	1,3
3	FSiS	2
4	SiSh	5
5	MS	4,6
6	WS	5,7
7	D	6,8
8	PS	6,7,9
9	BS	7,8

Let's clean up this dataset. The 'Well Name' and 'Formation' columns can be turned into a categorical data type.



In [2]:

    
training_data['Well Name'] = training_data['Well Name'].astype('category')
training_data['Formation'] = training_data['Formation'].astype('category')
training_data['Well Name'].unique()









    Out[2]:





[SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, KIMZEY A, CROSS H CATTLE, NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]
Categories (10, object): [SHRIMPLIN, ALEXANDER D, SHANKLE, LUKE G U, ..., NOLAN, Recruit F9, NEWBY, CHURCHMAN BIBLE]



In [3]:

    
# Drop the rows with missing PEF values
training_data.dropna(inplace=True)



In [4]:

    
training_data.describe()









    Out[4]:






  
    
      
      Facies
      Depth
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
    
  
  
    
      count
      3232.000000
      3232.000000
      3232.000000
      3232.000000
      3232.000000
      3232.000000
      3232.000000
      3232.000000
      3232.000000
    
    
      mean
      4.422030
      2875.824567
      66.135769
      0.642719
      3.559642
      13.483213
      3.725014
      1.498453
      0.520287
    
    
      std
      2.504243
      131.006274
      30.854826
      0.241845
      5.228948
      7.698980
      0.896152
      0.500075
      0.286792
    
    
      min
      1.000000
      2573.500000
      13.250000
      -0.025949
      -21.832000
      0.550000
      0.200000
      1.000000
      0.010000
    
    
      25%
      2.000000
      2791.000000
      46.918750
      0.492750
      1.163750
      8.346750
      3.100000
      1.000000
      0.273000
    
    
      50%
      4.000000
      2893.500000
      65.721500
      0.624437
      3.500000
      12.150000
      3.551500
      1.000000
      0.526000
    
    
      75%
      6.000000
      2980.000000
      79.626250
      0.812735
      6.432500
      16.453750
      4.300000
      2.000000
      0.767250
    
    
      max
      9.000000
      3122.500000
      361.150000
      1.480000
      18.600000
      84.400000
      8.094000
      2.000000
      1.000000

This is a quick view of the statistical distribution of the input variables. Looking at the count values, there are 3232 feature vectors in the training set.

These are the names of the 10 training wells in the Council Grove reservoir. Data has been recruited into pseudo-well 'Recruit F9' to better represent facies 9, the Phylloid-algal bafflestone.

Before we plot the well data, let's define a color map so the facies are represented by consistent color in all the plots in this tutorial. We also create the abbreviated facies labels, and add those to the facies_vectors dataframe.



In [5]:

    
# 1=sandstone  2=c_siltstone   3=f_siltstone 
# 4=marine_silt_shale 5=mudstone 6=wackestone 7=dolomite
# 8=packstone 9=bafflestone
facies_colors = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']
facies_labels = ['SS', 'CSiS', 'FSiS', 'SiSh', 'MS', 'WS', 'D','PS', 'BS']
#facies_color_map is a dictionary that maps facies labels
#to their respective colors
facies_color_map = {}
for ind, label in enumerate(facies_labels):
    facies_color_map[label] = facies_colors[ind]

def label_facies(row, labels):
    return labels[ row['Facies'] -1]
    
training_data.loc[:,'FaciesLabels'] = training_data.apply(lambda row: label_facies(row, facies_labels), axis=1)

Let's take a look at the data from individual wells in a more familiar log plot form. We will create plots for the five well log variables, as well as a log for facies labels. The plots are based on the those described in Alessandro Amato del Monte's excellent tutorial.



In [6]:

    
def make_facies_log_plot(logs, facies_colors):
    #make sure logs are sorted by depth
    logs = logs.sort_values(by='Depth')
    cmap_facies = colors.ListedColormap(
            facies_colors[0:len(facies_colors)], 'indexed')
    
    ztop=logs.Depth.min(); zbot=logs.Depth.max()
    
    cluster=np.repeat(np.expand_dims(logs['Facies'].values,1), 100, 1)
    
    f, ax = plt.subplots(nrows=1, ncols=6, figsize=(8, 12))
    ax[0].plot(logs.GR, logs.Depth, '-g')
    ax[1].plot(logs.ILD_log10, logs.Depth, '-')
    ax[2].plot(logs.DeltaPHI, logs.Depth, '-', color='0.5')
    ax[3].plot(logs.PHIND, logs.Depth, '-', color='r')
    ax[4].plot(logs.PE, logs.Depth, '-', color='black')
    im=ax[5].imshow(cluster, interpolation='none', aspect='auto',
                    cmap=cmap_facies,vmin=1,vmax=9)
    
    divider = make_axes_locatable(ax[5])
    cax = divider.append_axes("right", size="20%", pad=0.05)
    cbar=plt.colorbar(im, cax=cax)
    cbar.set_label((17*' ').join([' SS ', 'CSiS', 'FSiS', 'SiSh', ' MS ', ' WS ', ' D  ', ' PS ', ' BS ']))
    cbar.set_ticks(range(0,1)); cbar.set_ticklabels('')
    
    for i in range(len(ax)-1):
        ax[i].set_ylim(ztop,zbot)
        ax[i].invert_yaxis()
        ax[i].grid()
        ax[i].locator_params(axis='x', nbins=3)
    
    ax[0].set_xlabel("GR")
    ax[0].set_xlim(logs.GR.min(),logs.GR.max())
    ax[1].set_xlabel("ILD_log10")
    ax[1].set_xlim(logs.ILD_log10.min(),logs.ILD_log10.max())
    ax[2].set_xlabel("DeltaPHI")
    ax[2].set_xlim(logs.DeltaPHI.min(),logs.DeltaPHI.max())
    ax[3].set_xlabel("PHIND")
    ax[3].set_xlim(logs.PHIND.min(),logs.PHIND.max())
    ax[4].set_xlabel("PE")
    ax[4].set_xlim(logs.PE.min(),logs.PE.max())
    ax[5].set_xlabel('Facies')
    
    ax[1].set_yticklabels([]); ax[2].set_yticklabels([]); ax[3].set_yticklabels([])
    ax[4].set_yticklabels([]); ax[5].set_yticklabels([])
    ax[5].set_xticklabels([])
    f.suptitle('Well: %s'%logs.iloc[0]['Well Name'], fontsize=14,y=0.94)

Placing the log plotting code in a function will make it easy to plot the logs from multiples wells, and can be reused later to view the results when we apply the facies classification model to other wells. The function was written to take a list of colors and facies labels as parameters.

We then show log plots for wells SHRIMPLIN.



In [7]:

    
make_facies_log_plot(training_data[training_data['Well Name'] == 'SHRIMPLIN'], facies_colors)

In addition to individual wells, we can look at how the various facies are represented by the entire training set. Let's plot a histogram of the number of training examples for each facies class.



In [8]:

    
#count the number of unique entries for each facies, sort them by
#facies number (instead of by number of entries)
facies_counts = training_data['Facies'].value_counts().sort_index()
#use facies labels to index each count
facies_counts.index = facies_labels

facies_counts.plot(kind='bar',color=facies_colors, title='Distribution of Training Data by Facies')
facies_counts









    Out[8]:





SS      259
CSiS    738
FSiS    615
SiSh    184
MS      217
WS      462
D        98
PS      498
BS      161
Name: Facies, dtype: int64

This shows the distribution of examples by facies for the examples in the training set. Dolomite (facies 7) has the fewest with 81 examples. Depending on the performance of the classifier we are going to train, we may consider getting more examples of these facies.

Crossplots are a familiar tool in the geosciences to visualize how two properties vary with rock type. This dataset contains 5 log variables, and scatter matrix can help to quickly visualize the variation between the all the variables in the dataset. We can employ the very useful Seaborn library to quickly create a nice looking scatter matrix. Each pane in the plot shows the relationship between two of the variables on the x and y axis, with each point is colored according to its facies. The same colormap is used to represent the 9 facies.



In [9]:

    
#save plot display settings to change back to when done plotting with seaborn
inline_rc = dict(mpl.rcParams)

import seaborn as sns
sns.set()
sns.pairplot(training_data.drop(['Well Name','Facies','Formation','Depth','NM_M','RELPOS'],axis=1),
             hue='FaciesLabels', palette=facies_color_map,
             hue_order=list(reversed(facies_labels)))

#switch back to default matplotlib plot style
mpl.rcParams.update(inline_rc)

"Upsampling" the dataset

The supplied training data has a sampling rate of 0.5m, which is lower than the industry standard of 0.1524m. This means the the number of observations is a little on the small side, meaning that many ML classifiers will always perform poorly, especially with high entropy datasets.

One workaround to this will be to increase the sampling rate to 0.1m, by using a cubic spline to fill in the gaps in the data. Making up data is generally a no-no, but since wireline logs are generally heavily smoothed by the vendors, this additional step shouldn't add too much error, but will give us 5x more data to play with. We'll do this for each individual well (rather than the whole dataset) to prevent interpolation between wells.



In [10]:

    
upsampled_data = pd.DataFrame()
for well in training_data['Well Name'].unique():
    df = training_data[training_data['Well Name'] == well]
    df.index = np.arange(0, 5*len(df), 5)
    upsampled_df = pd.DataFrame(index=np.arange(0, 5*len(df)))
    upsampled_df = upsampled_df.join(df)
    upsampled_df.interpolate(method='cubic', limit=4, inplace=True)
    upsampled_df.fillna(method="pad", limit=4, inplace=True)
    upsampled_df.drop_duplicates(inplace=True)
    if len(upsampled_data) == 0:
        upsampled_data = upsampled_df
    else:
        upsampled_data = upsampled_data.append(upsampled_df, ignore_index=True)

upsampled_data["Facies"] = upsampled_data["Facies"].round()
upsampled_data["Facies"] = upsampled_data["Facies"].astype(int)
upsampled_data["NM_M"] = upsampled_data["NM_M"].round()
upsampled_data["NM_M"] = upsampled_data["NM_M"].astype(int)

# Sometimes a small number of the facies are labelled as 0 or 10 - these need to be removed
upsampled_data = upsampled_data[upsampled_data.Facies != 0]
upsampled_data = upsampled_data[upsampled_data.Facies != 10]
upsampled_data.loc[:,'FaciesLabels'] = upsampled_data.apply(lambda row: label_facies(row, facies_labels), axis=1)



In [11]:

    
upsampled_data









    Out[11]:






  
    
      
      Facies
      Formation
      Well Name
      Depth
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
      FaciesLabels
    
  
  
    
      0
      3
      A1 SH
      SHRIMPLIN
      2793.0
      77.450000
      0.664000
      9.900000
      11.915000
      4.600000
      1
      1.000000
      FSiS
    
    
      1
      3
      A1 SH
      SHRIMPLIN
      2793.1
      79.284515
      0.663545
      11.150359
      12.015714
      4.546545
      1
      0.996012
      FSiS
    
    
      2
      3
      A1 SH
      SHRIMPLIN
      2793.2
      79.937114
      0.662987
      12.190536
      12.139619
      4.460793
      1
      0.991895
      FSiS
    
    
      3
      3
      A1 SH
      SHRIMPLIN
      2793.3
      79.753455
      0.662357
      13.035533
      12.278167
      4.351767
      1
      0.987673
      FSiS
    
    
      4
      3
      A1 SH
      SHRIMPLIN
      2793.4
      79.079198
      0.661685
      13.700354
      12.422809
      4.228495
      1
      0.983367
      FSiS
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      16122
      5
      C LM
      CHURCHMAN BIBLE
      3122.1
      51.177622
      0.964821
      3.026104
      7.597464
      3.161609
      2
      0.659412
      MS
    
    
      16123
      5
      C LM
      CHURCHMAN BIBLE
      3122.2
      50.791689
      0.964974
      2.945037
      7.451181
      3.179267
      2
      0.657823
      MS
    
    
      16124
      5
      C LM
      CHURCHMAN BIBLE
      3122.3
      50.407644
      0.965716
      2.845018
      7.256165
      3.206420
      2
      0.656228
      MS
    
    
      16125
      5
      C LM
      CHURCHMAN BIBLE
      3122.4
      50.121933
      0.967306
      2.731266
      6.999433
      3.244515
      2
      0.654622
      MS
    
    
      16126
      5
      C LM
      CHURCHMAN BIBLE
      3122.5
      50.031000
      0.970000
      2.609000
      6.668000
      3.295000
      2
      0.653000
      MS
    
  

16122 rows × 12 columns

Let's check if the facies distributions still look right



In [12]:

    
upsampled_data.describe()









    Out[12]:






  
    
      
      Facies
      Depth
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
    
  
  
    
      count
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
    
    
      mean
      4.420233
      2875.779266
      66.132765
      0.642385
      3.562926
      13.488510
      3.724287
      1.498325
      0.520044
    
    
      std
      2.492889
      130.886683
      30.795497
      0.241821
      5.207364
      7.684145
      0.894569
      0.500013
      0.284700
    
    
      min
      1.000000
      2573.500000
      12.759470
      -0.026553
      -21.923002
      -1.112217
      0.058033
      1.000000
      -0.096527
    
    
      25%
      2.000000
      2791.000000
      46.939636
      0.490246
      1.144971
      8.359510
      3.113000
      1.000000
      0.279000
    
    
      50%
      4.000000
      2893.600003
      65.816922
      0.624654
      3.461482
      12.138445
      3.546221
      1.000000
      0.525477
    
    
      75%
      6.000000
      2980.000000
      79.864096
      0.812950
      6.483459
      16.492567
      4.306479
      2.000000
      0.761796
    
    
      max
      9.000000
      3122.500000
      375.701116
      1.487703
      20.032203
      84.606003
      8.094000
      2.000000
      1.201765



In [13]:

    
facies_counts = upsampled_data['Facies'].value_counts().sort_index()
facies_counts.index = facies_labels

facies_counts.plot(kind='bar',color=facies_colors, title='Distribution of Training Data by Facies')
facies_counts









    Out[13]:





SS      1321
CSiS    3650
FSiS    2967
SiSh     968
MS      1149
WS      2261
D        748
PS      2200
BS       858
Name: Facies, dtype: int64

Looks good! We'll now use this upsampled data as our training data



In [14]:

    
training_data = upsampled_data

Conditioning the data set

Now we extract just the feature variables we need to perform the classification. The predictor variables are the five wireline values and two geologic constraining variables. We also get a vector of the facies labels that correspond to each feature vector.



In [15]:

    
correct_facies_labels = training_data['Facies'].values
well_names = training_data['Well Name']

feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)
feature_vectors.describe()









    Out[15]:






  
    
      
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
    
  
  
    
      count
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
      16122.000000
    
    
      mean
      66.132765
      0.642385
      3.562926
      13.488510
      3.724287
      1.498325
      0.520044
    
    
      std
      30.795497
      0.241821
      5.207364
      7.684145
      0.894569
      0.500013
      0.284700
    
    
      min
      12.759470
      -0.026553
      -21.923002
      -1.112217
      0.058033
      1.000000
      -0.096527
    
    
      25%
      46.939636
      0.490246
      1.144971
      8.359510
      3.113000
      1.000000
      0.279000
    
    
      50%
      65.816922
      0.624654
      3.461482
      12.138445
      3.546221
      1.000000
      0.525477
    
    
      75%
      79.864096
      0.812950
      6.483459
      16.492567
      4.306479
      2.000000
      0.761796
    
    
      max
      375.701116
      1.487703
      20.032203
      84.606003
      8.094000
      2.000000
      1.201765

Scikit includes a preprocessing module that can 'standardize' the data (giving each variable zero mean and unit variance, also called whitening). Many machine learning algorithms assume features will be standard normally distributed data (ie: Gaussian with zero mean and unit variance). The factors used to standardize the training set must be applied to any subsequent feature set that will be input to the classifier. The StandardScalar class can be fit to the training set, and later used to standardize any training data.



In [16]:

    
from sklearn import preprocessing

scaler = preprocessing.StandardScaler().fit(feature_vectors)
scaled_features = scaler.transform(feature_vectors)



In [17]:

    
feature_vectors









    Out[17]:






  
    
      
      GR
      ILD_log10
      DeltaPHI
      PHIND
      PE
      NM_M
      RELPOS
    
  
  
    
      0
      77.450000
      0.664000
      9.900000
      11.915000
      4.600000
      1
      1.000000
    
    
      1
      79.284515
      0.663545
      11.150359
      12.015714
      4.546545
      1
      0.996012
    
    
      2
      79.937114
      0.662987
      12.190536
      12.139619
      4.460793
      1
      0.991895
    
    
      3
      79.753455
      0.662357
      13.035533
      12.278167
      4.351767
      1
      0.987673
    
    
      4
      79.079198
      0.661685
      13.700354
      12.422809
      4.228495
      1
      0.983367
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      16122
      51.177622
      0.964821
      3.026104
      7.597464
      3.161609
      2
      0.659412
    
    
      16123
      50.791689
      0.964974
      2.945037
      7.451181
      3.179267
      2
      0.657823
    
    
      16124
      50.407644
      0.965716
      2.845018
      7.256165
      3.206420
      2
      0.656228
    
    
      16125
      50.121933
      0.967306
      2.731266
      6.999433
      3.244515
      2
      0.654622
    
    
      16126
      50.031000
      0.970000
      2.609000
      6.668000
      3.295000
      2
      0.653000
    
  

16122 rows × 7 columns

Scikit also includes a handy function to randomly split the training data into training and test sets. The test set contains a small subset of feature vectors that are not used to train the network. Because we know the true facies labels for these examples, we can compare the results of the classifier to the actual facies and determine the accuracy of the model. Let's use 20% of the data for the test set.



In [18]:

    
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(scaled_features, correct_facies_labels, test_size=0.1, random_state=48)

Training the SVM classifier

Now we use the cleaned and conditioned training set to create a facies classifier. As mentioned above, we will use a type of machine learning model known as a support vector machine. The SVM is a map of the feature vectors as points in a multi dimensional space, mapped so that examples from different facies are divided by a clear gap that is as wide as possible.

The SVM implementation in scikit-learn takes a number of important parameters. First we create a classifier using the default settings.



In [19]:

    
from sklearn.svm import SVC

clf = SVC()
clf.fit(X_train, y_train)
predicted_labels = clf.predict(X_test)

Now we can train the classifier using the training set we created above.

Now that the model has been trained on our data, we can use it to predict the facies of the feature vectors in the test set. Because we know the true facies labels of the vectors in the test set, we can use the results to evaluate the accuracy of the classifier.

We need some metrics to evaluate how good our classifier is doing. A confusion matrix is a table that can be used to describe the performance of a classification model. Scikit-learn allows us to easily create a confusion matrix by supplying the actual and predicted facies labels.

The confusion matrix is simply a 2D array. The entries of confusion matrix C[i][j] are equal to the number of observations predicted to have facies j, but are known to have facies i.

To simplify reading the confusion matrix, a function has been written to display the matrix along with facies labels and various error metrics. See the file classification_utilities.py in this repo for the display_cm() function.



In [20]:

    
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from classification_utilities import display_cm, display_adj_cm

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS    75    55     4                 1                     135
     CSiS    12   282    47           1     1                     343
     FSiS     6    92   189     1           1                     289
     SiSh           3     8    64     3    26     1     1         106
       MS           2     1    11    33    55     3     5         110
       WS                 2     8    11   166     3    38     1   229
        D                 1     3     3    15    35    24     1    82
       PS                 2     5     2    41     7   168     3   228
       BS                                   3     2    19    67    91

Precision  0.81  0.65  0.74  0.70  0.62  0.54  0.69  0.66  0.93  0.68
   Recall  0.56  0.82  0.65  0.60  0.30  0.72  0.43  0.74  0.74  0.67
       F1  0.66  0.73  0.70  0.65  0.40  0.62  0.53  0.70  0.82  0.66

The rows of the confusion matrix correspond to the actual facies labels. The columns correspond to the labels assigned by the classifier. For example, consider the first row. For the feature vectors in the test set that actually have label SS, 23 were correctly indentified as SS, 21 were classified as CSiS and 2 were classified as FSiS.

The entries along the diagonal are the facies that have been correctly classified. Below we define two functions that will give an overall value for how the algorithm is performing. The accuracy is defined as the number of correct classifications divided by the total number of classifications.



In [21]:

    
def accuracy(conf):
    total_correct = 0.
    nb_classes = conf.shape[0]
    for i in np.arange(0,nb_classes):
        total_correct += conf[i][i]
    acc = total_correct/sum(sum(conf))
    return acc

As noted above, the boundaries between the facies classes are not all sharp, and some of them blend into one another. The error within these 'adjacent facies' can also be calculated. We define an array to represent the facies adjacent to each other. For facies label i, adjacent_facies[i] is an array of the adjacent facies labels.



In [22]:

    
adjacent_facies = np.array([[1], [0,2], [1], [4], [3,5], [4,6,7], [5,7], [5,6,8], [6,7]])

def accuracy_adjacent(conf, adjacent_facies):
    nb_classes = conf.shape[0]
    total_correct = 0.
    for i in np.arange(0,nb_classes):
        total_correct += conf[i][i]
        for j in adjacent_facies[i]:
            total_correct += conf[i][j]
    return total_correct / sum(sum(conf))



In [23]:

    
print('Facies classification accuracy = %f' % accuracy(conf))
print('Adjacent facies classification accuracy = %f' % accuracy_adjacent(conf, adjacent_facies))









    



Facies classification accuracy = 0.668940
Adjacent facies classification accuracy = 0.940484

Cross validation using leave P wells out

This function performs CV using the F1 score by leaving 1 well out. This is probably more appropraite for this dataset rather than CV based on a shuffled train/test dataset.



In [24]:

    
from sklearn.model_selection import LeavePGroupsOut
from sklearn.model_selection import GridSearchCV

def LPWO_CV(estimator, parameters, p=2):
    lpgo = LeavePGroupsOut(n_groups=p)
    clf = GridSearchCV(estimator, parameters, n_jobs=-1, verbose=3, scoring="f1_micro",
                       cv=lpgo.split(scaled_features, correct_facies_labels, groups=training_data['Well Name']))
    clf.fit(scaled_features, correct_facies_labels)
    return clf

SVM Tuning



In [25]:

    
parameters = {'C': [.01, 1, 5, 10, 20, 50, 100, 1000, 5000, 10000], 
              'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10], 
              'kernel': ['rbf']}  # This could be extended to the linear kernel but it takes a long time

clf_svr = SVC()
clf = LPWO_CV(clf_svr, parameters)









    



Fitting 28 folds for each of 60 candidates, totalling 1680 fits
[CV] gamma=0.0001, kernel=rbf, C=0.01 ................................
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[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.359264, total=  15.3s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.363492, total=  20.2s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.351809, total=  21.4s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................






    



[Parallel(n_jobs=-1)]: Done  80 tasks      | elapsed:  2.3min






    



[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.432370, total=  16.1s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.461346, total=  15.7s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.397796, total=  16.0s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.514716, total=  17.5s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.458524, total=  17.4s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.326073, total=  20.8s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.448884, total=  17.4s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.492289, total=  18.0s
[CV] gamma=0.1, kernel=rbf, C=0.01 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.272762, total=  23.2s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.299543, total=  23.7s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.378201, total=  16.5s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.246411, total=  24.0s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ... gamma=0.01, kernel=rbf, C=0.01, score=0.327252, total=  24.4s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.365957, total=  19.5s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.285109, total=  18.9s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.389323, total=  15.5s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.431106, total=  15.1s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.464007, total=  15.9s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.502202, total=  16.2s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.471081, total=  16.4s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.475297, total=  16.6s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.477864, total=  15.8s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.464631, total=  15.4s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.441107, total=  15.9s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.470315, total=  16.7s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.445148, total=  16.7s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.477238, total=  15.7s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.489571, total=  19.2s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.396675, total=  18.1s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.494391, total=  18.8s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.280363, total=  19.7s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.384944, total=  18.0s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=0.01, score=0.427774, total=  18.5s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.377984, total=  21.1s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.317688, total=  21.6s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.356181, total=  21.6s
[CV] gamma=1, kernel=rbf, C=0.01 .....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.303878, total=  20.7s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.396749, total=  20.1s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.320577, total=  21.7s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.324681, total=  25.2s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.308588, total=  19.4s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.257387, total=  20.1s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.307343, total=  19.5s
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.347542, total=  19.6s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.347355, total=  20.8s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.352699, total=  21.4s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.347667, total=  20.5s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.379568, total=  21.1s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.188073, total=  23.4s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.393428, total=  20.5s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.342707, total=  20.4s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.324823, total=  20.2s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.404247, total=  24.7s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.315539, total=  20.3s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.363404, total=  20.5s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.339795, total=  19.9s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.322629, total=  24.0s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.334857, total=  24.0s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.321304, total=  23.8s
[CV] ...... gamma=1, kernel=rbf, C=0.01, score=0.293001, total=  23.0s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.216755, total=  34.2s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.198146, total=  34.3s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.177572, total=  35.0s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.211995, total=  35.1s
[CV] gamma=10, kernel=rbf, C=0.01 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.265680, total=  31.6s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.170623, total=  33.2s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.195234, total=  32.9s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.247037, total=  32.9s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.121252, total=  33.8s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.241823, total=  31.9s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.262376, total=  32.3s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.230919, total=  33.5s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.266011, total=  34.1s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.118723, total=  44.0s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.247044, total=  32.7s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.225231, total=  33.5s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.205624, total=  32.7s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.245550, total=  34.7s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.227848, total=  32.3s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.246648, total=  41.6s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.239796, total=  33.6s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.218430, total=  44.2s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.358378, total=  19.3s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.184737, total=  42.8s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.320925, total=  19.4s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.350406, total=  20.4s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.262384, total=  34.2s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.359608, total=  19.7s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.411549, total=  18.9s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.285311, total=  19.9s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.388414, total=  18.8s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.243872, total=  44.2s
[CV] gamma=0.0001, kernel=rbf, C=1 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.402578, total=  18.7s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.347560, total=  19.3s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.222174, total=  35.8s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.349441, total=  19.7s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.388936, total=  23.8s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.359117, total=  19.0s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.348873, total=  19.5s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.173458, total=  44.5s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.301341, total=  22.0s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.391812, total=  20.4s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=10, kernel=rbf, C=0.01, score=0.213328, total=  44.3s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.332453, total=  20.2s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.414965, total=  20.0s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.439515, total=  23.9s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.381333, total=  19.6s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.297012, total=  19.4s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.343274, total=  19.6s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.354610, total=  13.6s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.351577, total=  20.4s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.505446, total=  14.9s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.272762, total=  23.2s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.426590, total=  14.9s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.364623, total=  20.6s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.487638, total=  15.4s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.326726, total=  19.5s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.433137, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.300789, total=  23.6s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.461763, total=  14.5s
[CV] gamma=0.001, kernel=rbf, C=1 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.448900, total=  15.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.410064, total=  14.4s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.380390, total=  13.9s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.258052, total=  24.2s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.364255, total=  16.9s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] .... gamma=0.0001, kernel=rbf, C=1, score=0.339166, total=  23.7s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.388901, total=  14.3s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.288638, total=  16.2s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.424299, total=  14.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.455551, total=  14.7s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.481702, total=  14.5s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.486350, total=  14.7s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.482477, total=  14.3s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.475126, total=  14.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.492605, total=  16.7s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.440463, total=  14.3s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.396720, total=  10.3s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.451889, total=  14.8s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.443565, total=  13.9s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.461967, total=  14.9s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.545539, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.469827, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.456110, total=  14.7s
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.504774, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.391009, total=  17.2s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.491187, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.488313, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.521150, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=1 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.407777, total=  11.0s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.486082, total=  16.8s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.407748, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.409531, total=  16.8s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.374468, total=  12.9s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ..... gamma=0.001, kernel=rbf, C=1, score=0.359721, total=  17.2s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.398607, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.433375, total=  10.6s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.277699, total=  12.2s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.444276, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.520357, total=  10.9s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.499339, total=  10.8s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................






    



[Parallel(n_jobs=-1)]: Done 240 tasks      | elapsed:  6.0min






    



[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.466136, total=  10.9s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.486764, total=  11.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.502417, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.441965, total=  10.9s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.436392, total=   8.6s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.541904, total=  12.2s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.510545, total=   9.2s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.419815, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.498377, total=  11.4s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.472370, total=   9.5s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.500980, total=  11.1s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.511628, total=   8.9s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.491892, total=   9.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.421987, total=  12.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.491068, total=  12.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.450856, total=  12.3s
[CV] gamma=0.1, kernel=rbf, C=1 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.498854, total=   9.2s
[CV] ...... gamma=0.01, kernel=rbf, C=1, score=0.420256, total=  12.7s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.534601, total=   9.7s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.379574, total=  10.4s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.434602, total=   9.2s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.405559, total=   8.3s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.393965, total=   8.5s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.446370, total=   9.1s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.436904, total=   8.9s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.386733, total=  10.2s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.497255, total=   8.9s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.478203, total=   9.0s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.523113, total=   9.1s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.469420, total=   8.9s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.454965, total=   9.0s
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.487449, total=   9.0s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.497681, total=   8.7s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.480434, total=   8.7s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.530148, total=  10.3s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.379876, total=   9.6s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.474499, total=  10.2s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.471379, total=  10.1s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.499238, total=   9.1s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.406705, total=  10.0s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.475887, total=   9.8s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.508517, total=  10.0s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.464494, total=  10.5s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.445828, total=   9.6s
[CV] gamma=1, kernel=rbf, C=1 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=1, score=0.540581, total=  10.6s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.404459, total=   9.7s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.498625, total=  10.7s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.368085, total=  11.5s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.346226, total=   9.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.348216, total=   9.7s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.352395, total=   9.4s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.405116, total=   9.8s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.386383, total=   9.6s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.354622, total=  11.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.468435, total=   9.7s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.439297, total=   9.5s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.490002, total=   9.6s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.446499, total=   9.8s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.512325, total=  11.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.405097, total=  10.0s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.466424, total=   9.8s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.512059, total=   9.8s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.461434, total=   9.7s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.437099, total=  11.5s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.466783, total=   9.8s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.518072, total=  11.4s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.498254, total=  11.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ......... gamma=1, kernel=rbf, C=1, score=0.532018, total=  11.5s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.328621, total=  55.5s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.219858, total=  56.9s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.265665, total=  58.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.267764, total=  54.2s
[CV] gamma=10, kernel=rbf, C=1 .......................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.346471, total=  55.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.253713, total=  52.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.341011, total=  57.0s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.273195, total=  59.6s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.238236, total=  53.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.199418, total=  55.9s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.185161, total=  56.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.294883, total=  54.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.371318, total=  53.3s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.320220, total=  54.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.312417, total=  53.8s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.312594, total=  55.4s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.252853, total=  59.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.316784, total=  57.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.327226, total=  57.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.191489, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.365913, total=  14.6s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.237826, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.495481, total=  15.4s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.425202, total=  15.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.448213, total=  15.0s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.446968, total=  14.5s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.460220, total=  15.7s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.441363, total=  15.8s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.441031, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.404450, total=  14.4s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.382141, total=  15.2s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.364255, total=  17.5s
[CV] gamma=0.0001, kernel=rbf, C=5 ...................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.378139, total=  13.7s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.332074, total= 1.3min
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.419554, total=  14.9s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.308398, total=  16.7s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.453166, total=  15.2s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.341088, total= 1.3min
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.462717, total=  15.2s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.464993, total=  15.5s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.485272, total=  16.1s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.466225, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.426186, total=  14.8s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.437245, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.465681, total=  17.5s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.367686, total=  11.1s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.524681, total=  11.5s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.440863, total=  15.5s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.426374, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.448555, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.501102, total=  12.6s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.459377, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.370986, total=  17.6s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.447860, total=  17.4s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.439718, total=  12.6s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.311920, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.367094, total=  17.8s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.361577, total=  58.4s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.465628, total=  12.8s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] .... gamma=0.0001, kernel=rbf, C=5, score=0.408414, total=  18.2s
[CV] gamma=0.001, kernel=rbf, C=5 ....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.375745, total=  14.2s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.480725, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.413807, total=  11.9s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.364850, total=  11.8s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.397341, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.436469, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.460321, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.507091, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.494936, total=  11.9s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.276288, total=  12.6s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.459901, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.469877, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.496046, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.436602, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.490028, total=  12.5s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.455230, total=  10.3s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.504361, total=  14.0s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.410767, total=  11.9s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.502723, total=  11.8s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.387193, total= 1.3min
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.421987, total=  14.1s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ........ gamma=10, kernel=rbf, C=1, score=0.427629, total= 1.4min
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.493560, total=  13.7s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.393093, total=  13.7s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ..... gamma=0.001, kernel=rbf, C=5, score=0.389054, total=  14.4s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.543917, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.501734, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.504039, total=  10.1s
[CV] gamma=0.01, kernel=rbf, C=5 .....................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.493067, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.511457, total=  10.8s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.430072, total=   9.4s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.526360, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.468289, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.375745, total=  12.0s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.470297, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.389745, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.437771, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.409668, total=  11.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.489872, total=  10.5s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.511436, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.495592, total=  10.4s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.485623, total=  10.0s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.479789, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.470929, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.564278, total=  11.6s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.468077, total=  11.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.423094, total=   8.6s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.448541, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.482143, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.498725, total=   8.6s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.478327, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.503305, total=   8.9s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.461272, total=   9.1s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.492730, total=  11.6s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.465930, total=   8.6s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.477086, total=   9.3s
[CV] gamma=0.1, kernel=rbf, C=5 ......................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.443927, total=  12.0s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.438345, total=   8.4s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.535976, total=   9.2s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ...... gamma=0.01, kernel=rbf, C=5, score=0.497394, total=  11.8s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.397021, total=  10.3s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.388050, total=   8.1s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.381515, total=   8.8s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.460396, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.443842, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.483532, total=   8.4s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.393790, total=  10.2s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.484808, total=   9.0s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.505268, total=   8.8s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.466454, total=   8.2s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.458260, total=   8.6s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.530906, total=  10.0s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.498820, total=   8.8s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.498756, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.380541, total=   8.3s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.506725, total=   9.2s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.489233, total=  10.2s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.438470, total=   8.9s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.390520, total=   8.7s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.459486, total=   9.0s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.415511, total=   8.9s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.518623, total=   9.1s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.508517, total=   9.7s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.374036, total=   8.7s
[CV] gamma=1, kernel=rbf, C=5 ........................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.469982, total=   8.9s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.340403, total=   8.6s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.492433, total=  10.3s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.349787, total=  10.5s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.328737, total=   8.6s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ....... gamma=0.1, kernel=rbf, C=5, score=0.555845, total=  10.4s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.341422, total=   8.4s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.386964, total=   8.4s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.364267, total=   8.9s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.435956, total=   8.7s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.370501, total=  10.5s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.410172, total=   8.9s
[CV] gamma=10, kernel=rbf, C=5 .......................................






    



[Parallel(n_jobs=-1)]: Done 464 tasks      | elapsed: 10.1min






    



[CV] ......... gamma=1, kernel=rbf, C=5, score=0.442485, total=   9.2s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.411228, total=   8.8s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.445613, total=   9.0s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.480470, total=  10.3s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.392355, total=   9.7s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.437458, total=   8.5s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.471243, total=   9.3s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.424632, total=  10.5s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.435853, total=   9.0s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.492730, total=  10.5s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.492821, total=  10.2s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ......... gamma=1, kernel=rbf, C=5, score=0.504840, total=  10.3s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.224069, total=  58.1s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.327926, total=  59.1s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.270890, total=  56.3s
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.279315, total= 1.0min
[CV] gamma=10, kernel=rbf, C=5 .......................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.271676, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.340306, total=  59.4s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.351512, total=  59.7s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.258870, total=  55.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.242878, total=  56.8s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.205240, total=  59.5s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.190633, total=  59.8s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.309775, total=  54.9s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.376263, total=  56.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.320677, total=  58.0s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.297918, total=  59.6s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.315246, total=  59.7s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.314310, total=  59.5s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.253994, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.324675, total=  59.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.354832, total=  12.8s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.195319, total= 1.4min
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.504751, total=  13.7s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.243472, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.427052, total=  13.2s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.410688, total=  12.4s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.461763, total=  13.2s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.487148, total=  14.1s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.433608, total=  14.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.447984, total=  13.4s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.443307, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.383016, total=  13.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.363404, total=  15.3s
[CV] gamma=0.0001, kernel=rbf, C=10 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.388901, total=  12.5s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.425124, total=  13.5s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.338496, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.287932, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.454683, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.479414, total=  13.9s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.348151, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.485469, total=  13.8s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.480542, total=  13.9s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.474897, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.450791, total=  13.2s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.440678, total=  13.2s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.444696, total=  13.1s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.489951, total=  15.6s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.459879, total=  14.4s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.391622, total=  11.3s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.390631, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.455239, total=  13.8s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.540209, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.464740, total=  11.9s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.484005, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.497919, total=  12.2s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.469523, total=  11.2s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.478496, total=  12.5s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.358556, total=  16.1s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=10, score=0.407669, total=  15.9s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.318706, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.375745, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=10 ...................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.368547, total= 1.0min
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.518025, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.412144, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.395231, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.381484, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.442657, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.437554, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.510293, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.275935, total=  12.4s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.499780, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.454741, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.472159, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.508568, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.437245, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.505499, total=  13.6s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.400588, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.501391, total=  12.1s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.416320, total=  13.1s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.450576, total=   9.7s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.477773, total=  12.9s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.502723, total=  11.8s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.540672, total=  10.5s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.414823, total=  13.7s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.500347, total=  10.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=10, score=0.397245, total=  13.5s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.393150, total= 1.4min
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.505753, total=  10.3s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.492597, total=  10.8s
[CV] gamma=0.01, kernel=rbf, C=10 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.511687, total=  10.8s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.526776, total=  10.0s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ........ gamma=10, kernel=rbf, C=5, score=0.426853, total= 1.5min
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.468289, total=   9.7s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.430948, total=   9.5s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.373191, total=  11.3s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.471535, total=   9.8s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.385102, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.421726, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.410727, total=  11.5s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.502745, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.479304, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.503548, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.477864, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.466828, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.553659, total=  11.6s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.479940, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.392287, total=   8.3s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.456232, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.495944, total=   9.3s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.491500, total=  11.4s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.481447, total=  10.5s
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.492044, total=   8.7s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.445780, total=   9.1s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.461555, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.463220, total=   9.4s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.475307, total=   8.6s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.489821, total=  11.8s
[CV] gamma=0.1, kernel=rbf, C=10 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.405074, total=   8.3s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.519707, total=   9.5s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.436942, total=  11.7s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.378201, total=   8.0s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.382553, total=  10.2s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=10, score=0.490692, total=  11.8s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.378139, total=   8.6s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.449670, total=   8.6s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.365914, total=  10.0s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.431917, total=   8.5s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.484218, total=   8.6s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.484148, total=   9.1s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.501398, total=   9.2s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.461205, total=   8.6s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.449253, total=   8.9s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.512059, total=   9.0s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.517271, total=   9.4s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.368351, total=   8.2s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.533182, total=  10.4s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.428273, total=   8.4s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.487446, total=   9.0s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.447736, total=   8.3s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.387052, total=   8.7s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.398590, total=   8.6s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.498130, total=  10.2s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.516663, total=   9.5s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.493389, total=  10.7s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.353199, total=   8.3s
[CV] gamma=1, kernel=rbf, C=10 .......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.464940, total=   8.9s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.500970, total=  10.5s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.351915, total=  10.2s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.330006, total=   8.7s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.308820, total=   8.4s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=10, score=0.555101, total=  10.7s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.319477, total=   8.3s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.379125, total=   8.2s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.341067, total=   8.7s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.412168, total=   8.5s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.350741, total=  10.1s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.384192, total=   8.6s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.386581, total=   8.5s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.443561, total=   9.0s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.378076, total=   8.5s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.444754, total=   8.7s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.456959, total=   9.9s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.415969, total=   8.2s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.445269, total=   8.9s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.402720, total=  10.1s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.431279, total=   8.5s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.469049, total=  10.2s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.501117, total=  10.0s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ........ gamma=1, kernel=rbf, C=10, score=0.474971, total=  10.6s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.225399, total=  57.4s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.270890, total=  54.7s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.328853, total=  58.8s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.351512, total=  57.8s
[CV] gamma=10, kernel=rbf, C=10 ......................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.339365, total=  59.5s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.270751, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.261139, total=  56.1s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.280049, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.193697, total=  58.4s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.242034, total=  57.4s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.309335, total=  54.7s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.205240, total=  59.8s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.377768, total=  54.8s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.320448, total=  58.4s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.296618, total=  59.6s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.313881, total=  59.4s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.312170, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.254906, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.324443, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.358378, total=  11.8s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.499884, total=  12.6s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.414223, total=  11.9s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.480725, total=  11.7s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.194468, total= 1.4min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.440000, total=  12.9s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.488127, total=  12.5s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.427027, total=  13.0s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.245942, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.448213, total=  12.9s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.367258, total=  12.5s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.443307, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.368511, total=  14.1s
[CV] gamma=0.0001, kernel=rbf, C=20 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.424299, total=  11.7s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.397552, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.280169, total=  14.4s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.337363, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.471596, total=  13.5s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.490393, total=  13.2s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.350229, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.493835, total=  12.9s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.476027, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.468736, total=  13.4s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.474517, total=  12.9s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.431023, total=  12.9s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.427053, total=  13.0s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.427527, total=  11.1s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.482375, total=  13.5s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.499431, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.541136, total=  11.9s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.470704, total=  12.6s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.403098, total=  15.1s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.493758, total=  11.6s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.395011, total=  14.1s
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.498546, total=  14.8s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=20, score=0.363989, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.476994, total=  12.5s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.473419, total=  11.8s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.491187, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.318028, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.376596, total=  14.3s
[CV] gamma=0.001, kernel=rbf, C=20 ...................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.368547, total=  59.1s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.526568, total=  10.6s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.434602, total=  10.6s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.421099, total=  10.5s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.396075, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.432350, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.481023, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.458181, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.504844, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.509835, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.274171, total=  12.8s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.472159, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.507250, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.431452, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.398326, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.529010, total=  13.4s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.509276, total=  12.0s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.442819, total=   9.7s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.480266, total=  12.1s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.412920, total=  13.6s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.536732, total=  10.0s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.489942, total=  10.1s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.503812, total=  12.5s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.415988, total=  13.9s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.508201, total=   9.8s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=20, score=0.422934, total=  12.9s
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.395383, total= 1.4min
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.461426, total=   9.5s
[CV] gamma=0.01, kernel=rbf, C=20 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.524901, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.493772, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.510999, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.428321, total=   9.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ....... gamma=10, kernel=rbf, C=10, score=0.425689, total= 1.5min
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.384891, total=  10.4s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.468853, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.369787, total=  12.1s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.418474, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.494739, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.409668, total=  11.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.473800, total=  10.8s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.513223, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.473300, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.457821, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.486162, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.539628, total=  12.3s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.468446, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.378546, total=   8.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.482375, total=  10.5s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................






    



[Parallel(n_jobs=-1)]: Done 752 tasks      | elapsed: 16.4min






    



[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.499056, total=  11.8s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.488992, total=   9.7s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.434220, total=   9.5s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.476132, total=   9.5s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.460466, total=  10.6s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.473224, total=   8.9s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.440188, total=   9.6s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.497715, total=  12.0s
[CV] gamma=0.1, kernel=rbf, C=20 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.382616, total=   8.6s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.506645, total=   9.7s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.366163, total=   8.3s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.364681, total=  10.8s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.496649, total=  12.1s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=20, score=0.438882, total=  12.6s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.379616, total=   9.1s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.432343, total=   9.3s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.419558, total=   8.7s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.362385, total=  10.7s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.474382, total=   8.9s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.491413, total=   9.5s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.457782, total=   8.5s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.497742, total=   9.1s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.444859, total=   9.2s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.515984, total=   9.2s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.360594, total=   7.9s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.531286, total=  11.1s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.510668, total=   9.0s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.409502, total=   8.2s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.373410, total=   8.5s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.436230, total=   8.2s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.477041, total=   9.3s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.484700, total=  10.9s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.399060, total=   8.5s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.342780, total=   7.8s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.468378, total=   8.5s
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.513614, total=   9.7s
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] gamma=1, kernel=rbf, C=20 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.503947, total=  10.6s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.351064, total=  10.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.314410, total=   8.2s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.301817, total=   8.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.532014, total=  11.5s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=20, score=0.542070, total=  11.3s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.304073, total=   8.2s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.379744, total=   8.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.327190, total=   8.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.346507, total=   9.6s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.400274, total=   8.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.430875, total=   8.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.380889, total=   8.3s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.356686, total=   8.2s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.370826, total=   8.5s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.431023, total=   8.9s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.443307, total=   9.8s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.418599, total=   8.4s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.405564, total=   8.4s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.389498, total=  10.2s
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.436071, total=   8.7s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.446199, total=   9.7s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.498511, total=   9.8s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ........ gamma=1, kernel=rbf, C=20, score=0.462941, total=   9.9s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.227615, total=  58.1s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.328621, total=  57.6s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.269223, total=  54.5s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.262995, total=  54.9s
[CV] gamma=10, kernel=rbf, C=20 ......................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.284455, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.341011, total=  59.0s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.351054, total=  58.8s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.196323, total=  57.8s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.269364, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.238447, total=  56.5s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.205656, total=  59.4s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.306253, total=  54.9s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.378843, total=  54.4s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.296401, total=  58.2s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.323193, total=  57.2s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.313452, total=  58.5s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.310852, total=  59.8s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.326299, total=  58.4s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.253537, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.365691, total=  11.8s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.438073, total=  11.5s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.523754, total=  12.0s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.448555, total=  13.0s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.461045, total=  11.6s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.500612, total=  12.4s
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.479475, total=  11.6s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.415471, total=  11.5s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.195319, total= 1.4min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.245942, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.366601, total=  11.7s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.444824, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.374043, total=  14.1s
[CV] gamma=0.0001, kernel=rbf, C=50 ..................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.277699, total=  12.7s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.436469, total=  11.6s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.396919, total=  11.8s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.336985, total= 1.3min
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.454467, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.504803, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.352721, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.459256, total=  11.6s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.493615, total=  11.9s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.436387, total=  11.8s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.466225, total=  12.4s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.495826, total=  12.7s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.407826, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.488868, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.500569, total=  14.0s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.448360, total=  11.0s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.500545, total=  13.0s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.425009, total=  14.2s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.545539, total=  11.9s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.488324, total=  12.1s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.493560, total=  13.8s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.390764, total=  14.5s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] ... gamma=0.0001, kernel=rbf, C=50, score=0.386821, total=  14.2s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.503550, total=  11.6s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.489072, total=  11.8s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.373191, total=  13.2s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.480293, total=  12.2s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.528652, total=  10.8s
[CV] gamma=0.001, kernel=rbf, C=50 ...................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.418910, total=  10.4s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.460179, total=  10.8s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.395020, total=  11.1s
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.320516, total= 1.1min
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.494431, total=  10.6s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.368765, total= 1.0min
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.425412, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.517155, total=  10.8s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.373324, total=  12.7s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.470221, total=  10.8s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.502422, total=  12.0s
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.476495, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.509886, total=  11.8s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.431238, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.557831, total=  12.3s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.511596, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.406243, total=  11.2s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.420098, total=  12.6s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.485667, total=  13.0s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.429078, total=  10.6s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.520510, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.394267, total= 1.3min
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.489249, total=   9.9s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.438882, total=  13.2s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.499673, total=  12.5s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.499388, total=  10.7s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] .... gamma=0.001, kernel=rbf, C=50, score=0.458302, total=  13.5s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.491187, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=50 ....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.513291, total=  11.0s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.523234, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.419348, total=   9.5s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.449574, total=  10.0s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ....... gamma=10, kernel=rbf, C=20, score=0.419868, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.392766, total=  12.2s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.384891, total=  11.0s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.455033, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.408933, total=  10.8s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.490165, total=  10.4s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.397318, total=  12.2s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.482387, total=  11.5s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.516018, total=  10.6s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.465997, total=  10.7s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.533561, total=  12.4s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.481013, total=  10.6s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.457821, total=  11.1s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.389184, total=   9.9s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.471707, total=  10.5s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.477720, total=  10.4s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.483944, total=  12.0s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.427514, total=  10.2s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.476246, total=  10.3s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.462426, total=  11.1s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.467075, total=  10.1s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.476766, total=   9.9s
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.427262, total=  10.4s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.492730, total=  12.1s
[CV] gamma=0.1, kernel=rbf, C=50 .....................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.395508, total=   9.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.366382, total=   9.1s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.496334, total=  11.0s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.444315, total=  12.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ..... gamma=0.01, kernel=rbf, C=50, score=0.498138, total=  12.6s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.361277, total=  12.6s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.426980, total=   9.8s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.365478, total=  10.3s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.415438, total=   9.7s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.466377, total=   9.7s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.396613, total=  12.2s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.488551, total=  10.6s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.446143, total=   9.7s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.353280, total=   7.7s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.485272, total=  10.6s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.439807, total=   9.8s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.406721, total=   8.1s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.353526, total=   8.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.501180, total=  10.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.498145, total=  10.3s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.411506, total=   8.4s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.453743, total=  10.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.398355, total=   8.1s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.342155, total=   8.0s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.518392, total=  12.5s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.478231, total=   8.2s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.503594, total=  10.9s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.476388, total=  12.3s
[CV] gamma=1, kernel=rbf, C=50 .......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.338723, total=  10.2s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.295072, total=   7.9s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.501870, total=  12.0s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.294156, total=   8.1s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.536858, total=  13.0s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ...... gamma=0.1, kernel=rbf, C=50, score=0.532790, total=  13.6s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.379332, total=   7.9s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.340861, total=  10.2s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.296687, total=   8.3s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.323504, total=   8.5s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.392955, total=   8.4s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.378468, total=   8.1s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.344363, total=   8.2s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.429155, total=   8.9s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.362698, total=   8.6s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.422012, total=   8.7s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.377969, total=   8.4s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.391698, total=   8.3s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.441790, total=  10.0s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.432585, total=   8.5s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.370230, total=  10.1s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.417532, total=   9.7s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.452464, total=  10.0s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ........ gamma=1, kernel=rbf, C=50, score=0.517870, total=  10.1s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.228059, total=  58.7s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.326999, total=  58.3s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.268598, total=  55.0s
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.288862, total= 1.0min
[CV] gamma=10, kernel=rbf, C=50 ......................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.352658, total=  56.9s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.264026, total=  54.8s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.267746, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.206488, total=  58.3s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.317041, total=  53.6s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.236337, total=  57.2s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.377768, total=  54.4s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.341246, total=  59.9s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.201795, total=  59.5s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.326395, total=  58.1s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.297051, total=  59.6s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.312165, total=  59.2s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.254222, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.308216, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.331169, total=  59.2s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.389184, total=  11.6s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.538123, total=  11.2s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.512190, total=  10.9s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.464509, total=  12.5s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.194468, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.496206, total=  12.4s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.467232, total=  11.9s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.412560, total=  11.6s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.473796, total=  12.6s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.369665, total=  11.5s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.247706, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.442169, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.374894, total=  14.0s
[CV] gamma=0.0001, kernel=rbf, C=100 .................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.394598, total=  11.8s
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.445338, total=  11.1s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.434519, total=  11.7s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.503431, total=  11.0s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.278758, total=  14.2s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.333963, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.357707, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.494496, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.451516, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.468508, total=  12.7s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.435958, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.507469, total=  12.6s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.495594, total=  12.4s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.503982, total=  13.9s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.398326, total=  12.3s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.450798, total=  11.7s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.501851, total=  12.1s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.543685, total=  11.8s
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.420854, total=  14.6s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.477358, total=  14.6s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.505508, total=  10.6s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.494798, total=  12.0s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.409391, total=  14.3s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] .. gamma=0.0001, kernel=rbf, C=100, score=0.391660, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.494007, total=  12.5s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.489230, total=  12.1s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.373617, total=  13.6s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.528860, total=  11.0s
[CV] gamma=0.001, kernel=rbf, C=100 ..................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.462258, total=  10.8s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.422412, total=  10.3s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.323230, total= 1.1min
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.369201, total= 1.0min
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.395442, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.489274, total=  10.7s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.431483, total=  11.1s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.520357, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.397671, total=  13.1s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.476457, total=  11.0s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.499339, total=  11.6s
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.473756, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.440893, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.505272, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.410088, total=  10.9s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.510204, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.573000, total=  13.8s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.432187, total=  13.2s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.487744, total=  13.8s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.422207, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.498149, total=  11.5s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.457897, total=  13.9s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.511703, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.490173, total=  11.4s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.502326, total=  11.3s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ... gamma=0.001, kernel=rbf, C=100, score=0.494043, total=  13.7s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.485076, total=  11.7s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.516957, total=  11.6s
[CV] gamma=0.01, kernel=rbf, C=100 ...................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.397245, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.417597, total=   9.7s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.517816, total=  11.3s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.445831, total=  11.1s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.382359, total=  11.2s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ....... gamma=10, kernel=rbf, C=50, score=0.418316, total= 1.5min
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.397447, total=  13.4s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.445132, total=  11.0s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.420425, total=  11.4s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.387791, total=  13.3s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.497941, total=  11.1s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.484148, total=  12.1s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.520748, total=  11.8s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.458923, total=  11.2s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.532044, total=  13.7s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.461555, total=  11.9s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.482729, total=  11.2s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.469388, total=  11.2s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.393617, total=  11.0s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.482018, total=  11.4s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.468830, total=  11.5s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.421272, total=  12.3s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.490744, total=  13.6s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.467075, total=  11.4s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.459159, total=  11.8s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.459679, total=  11.2s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.402578, total=  10.6s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.483174, total=  13.7s
[CV] gamma=0.1, kernel=rbf, C=100 ....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.408696, total=  12.1s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.488772, total=  12.5s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.352812, total=  10.2s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.497766, total=  13.4s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] .... gamma=0.01, kernel=rbf, C=100, score=0.450912, total=  14.1s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.361469, total=  11.4s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.353617, total=  15.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.410272, total=  11.8s
[CV] gamma=1, kernel=rbf, C=100 ......................................






    



[Parallel(n_jobs=-1)]: Done 1104 tasks      | elapsed: 23.3min






    



[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.403296, total=  11.4s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.407198, total=  14.5s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.462031, total=  11.5s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.350177, total=   7.7s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.475121, total=  12.4s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.394670, total=   7.9s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.455728, total=  11.5s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.354682, total=   8.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.455386, total=  11.8s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.405386, total=   8.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.452329, total=  11.5s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.499304, total=  11.7s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.477151, total=  12.3s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.458720, total=  12.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.396710, total=   8.1s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.466544, total=   8.4s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.510808, total=  15.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.347156, total=   8.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.320000, total=  10.7s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.476011, total=  15.0s
[CV] gamma=1, kernel=rbf, C=100 ......................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.490307, total=  12.9s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.291329, total=   7.9s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.289998, total=   8.1s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.501039, total=  14.8s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.299008, total=   7.8s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.384488, total=   7.6s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.526581, total=  15.8s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ..... gamma=0.1, kernel=rbf, C=100, score=0.533135, total=  15.7s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.322420, total=   8.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.381976, total=   8.2s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.340508, total=  10.1s
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.427865, total=   8.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.339571, total=   8.1s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.370321, total=   8.6s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.436387, total=   8.1s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.364455, total=   8.5s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.439515, total=  10.5s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.372314, total=   8.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.382653, total=   8.5s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.382320, total=  10.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.436506, total=   8.3s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.414209, total=   9.9s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.456733, total=  10.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ....... gamma=1, kernel=rbf, C=100, score=0.514147, total=  10.3s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.268806, total=  54.1s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.326999, total=  57.9s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.229832, total=  58.7s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.266089, total=  54.0s
[CV] gamma=10, kernel=rbf, C=100 .....................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.340541, total=  58.2s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.289841, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.268208, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.353346, total=  58.2s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.377983, total=  53.4s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.237392, total=  57.5s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.207943, total=  58.9s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.296834, total=  57.9s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.325407, total=  55.0s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.202889, total=  58.6s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.326624, total=  57.9s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.254906, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.313023, total=  58.7s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.308875, total=  59.1s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.330473, total=  58.5s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.445479, total=  14.1s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.527193, total=  12.4s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.495961, total=  13.3s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.409061, total=  12.9s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.545075, total=  13.7s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.465793, total=  13.3s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.194894, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.468836, total=  14.2s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.491329, total=  15.2s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.250529, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.492832, total=  15.7s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.441790, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.396075, total=  14.0s
[CV] gamma=0.0001, kernel=rbf, C=1000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.497112, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.426279, total=  13.6s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.369442, total=  16.1s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.372766, total=  18.7s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.496340, total=  12.4s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.334719, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.358122, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.451086, total=  13.9s
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.465769, total=  13.5s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.503082, total=  14.5s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.514279, total=  14.4s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.423729, total=  14.1s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.507189, total=  14.4s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.393802, total=  14.0s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.559348, total=  16.3s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.539513, total=  12.6s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.420854, total=  17.0s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.500347, total=  12.4s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.477358, total=  16.2s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.507297, total=  15.0s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.453901, total=  16.9s
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.503550, total=  13.5s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.432986, total=  17.3s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.491422, total=  14.2s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=1000, score=0.427629, total=  17.6s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.507333, total=  14.9s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.367660, total=  17.3s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.368112, total=  59.3s
[CV] gamma=0.001, kernel=rbf, C=1000 .................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.325266, total= 1.0min
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.472240, total=  12.6s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.427884, total=  12.6s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.526360, total=  14.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.386157, total=  14.4s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.414137, total=  12.8s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.466172, total=  13.7s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.497484, total=  13.3s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.476222, total=  12.8s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.416020, total=  16.5s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.511933, total=  13.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.478092, total=  12.9s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.476078, total=  13.1s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.469684, total=  13.4s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.457136, total=  13.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.487013, total=  14.3s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.548350, total=  17.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.491500, total=  16.8s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.490652, total=  16.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.463516, total=  14.7s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.398362, total= 1.4min
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.409796, total=  18.5s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.436554, total=  17.4s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=1000, score=0.482502, total=  16.8s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ...... gamma=10, kernel=rbf, C=100, score=0.415988, total= 1.5min
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.500579, total=  20.3s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.500367, total=  19.2s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.481387, total=  21.6s
[CV] gamma=0.01, kernel=rbf, C=1000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.392646, total=  17.0s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.521540, total=  21.4s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.466745, total=  21.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.442088, total=  18.7s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.495728, total=  19.4s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.446163, total=  18.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.377295, total=  19.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.459454, total=  19.5s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.489936, total=  18.6s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.393191, total=  26.3s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.407904, total=  24.2s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.513653, total=  19.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.479745, total=  20.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.473756, total=  19.7s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.483802, total=  19.3s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.465729, total=  21.5s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.521805, total=  26.5s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.487245, total=  20.1s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.513232, total=  19.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.505478, total=  26.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.518488, total=  25.4s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.515138, total=  22.6s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.368794, total=  25.1s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.429432, total=  27.9s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.427173, total=  27.7s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.428439, total=  29.0s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.547655, total=  27.5s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=1000, score=0.472255, total=  28.6s
[CV] gamma=0.1, kernel=rbf, C=1000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.398004, total=  25.0s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.351937, total=  25.3s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.430298, total=  27.9s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.389659, total=  30.0s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.471815, total=  30.5s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.373591, total=  25.6s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.407384, total=  27.6s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.344798, total=  28.1s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.351729, total=   7.6s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.404392, total=  27.1s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.402086, total=   7.9s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.345896, total=   7.9s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.329362, total=  41.1s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.402693, total=   8.5s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.432451, total=  26.2s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.411933, total=  31.5s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.415395, total=  29.8s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.441426, total=  38.1s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.402350, total=   8.5s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.322128, total=  10.6s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.351532, total=   8.0s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.476398, total=   8.5s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.308172, total=   7.8s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.305975, total=   7.7s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.436731, total=  29.3s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.321798, total=   8.4s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.389645, total=   8.3s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.464493, total=  29.8s
[CV] gamma=1, kernel=rbf, C=1000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.322853, total=   8.3s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.382434, total=   8.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.348271, total=  10.3s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.455705, total=  28.6s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.440172, total=  27.4s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.439690, total=   8.3s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.363716, total=   8.7s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.463785, total=  44.3s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.340940, total=   8.6s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.358524, total=   8.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.422012, total=   8.6s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.484322, total=  40.0s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.450512, total=  10.7s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.371742, total=  10.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.378247, total=   9.1s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.407536, total=  32.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.376385, total=   8.6s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.422933, total=  10.9s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.439991, total=   8.9s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.472788, total=  41.0s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.461001, total=  11.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=1000, score=0.503723, total=  11.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.473419, total=  43.8s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=1000, score=0.483619, total=  44.0s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.229832, total=  57.5s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.326999, total=  59.3s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.269014, total=  56.2s
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.268439, total= 1.0min
[CV] gamma=10, kernel=rbf, C=1000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.266295, total=  56.6s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.340071, total=  59.9s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.203327, total=  59.2s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.353575, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.237392, total=  57.8s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.377983, total=  55.0s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.325187, total=  55.4s
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.289841, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.208359, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.326167, total=  58.0s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.296618, total=  59.7s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.313238, total=  59.7s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.309095, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.255819, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.330937, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.251235, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.195319, total= 1.4min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.463922, total=  15.1s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.502081, total=  15.0s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.545539, total=  16.0s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.441031, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.413001, total=  14.4s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.524276, total=  15.5s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.490636, total=  16.9s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.456560, total=  19.6s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.485793, total=  16.5s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.492597, total=  17.4s
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.334719, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=5000 ................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.393790, total=  18.6s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.497112, total=  16.3s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.426279, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.376170, total=  22.9s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.394598, total=  18.0s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.510064, total=  15.2s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.357707, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.469420, total=  14.7s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.470651, total=  15.8s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.427591, total=  15.3s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.503963, total=  16.8s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.510545, total=  16.3s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.401945, total=  15.1s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.515538, total=  16.2s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.573000, total=  19.2s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.425765, total=  18.8s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.481512, total=  19.4s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.505119, total=  16.5s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.475056, total=  19.5s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.325266, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] . gamma=0.0001, kernel=rbf, C=5000, score=0.455568, total=  21.6s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.525608, total=  19.4s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.491065, total=  18.6s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.368112, total=  59.4s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.489249, total=  19.8s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.432181, total=  24.4s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.508708, total=  20.6s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.494947, total=  20.9s
[CV] gamma=0.001, kernel=rbf, C=5000 .................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.422412, total=  17.6s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.452069, total=  18.4s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.392766, total=  26.5s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.523859, total=  21.1s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.448226, total=  20.3s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.408933, total=  19.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.489021, total=  19.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.385313, total=  23.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.398734, total= 1.4min
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.467595, total=  19.1s
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.516878, total=  20.4s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.476222, total=  20.7s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.407904, total=  27.8s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.455844, total=  19.8s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.477794, total=  19.7s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ..... gamma=10, kernel=rbf, C=1000, score=0.415600, total= 1.5min
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.474327, total=  19.8s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.476113, total=  20.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.529769, total=  26.5s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.490744, total=  26.0s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.503947, total=  25.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.443151, total=  27.5s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.453932, total=  22.2s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.486969, total=  27.6s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.377660, total=  45.8s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.461503, total=  47.0s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.486169, total=  44.0s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.487601, total=  50.7s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.478641, total=  47.2s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.475206, total=  49.4s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.383235, total=  42.1s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.406114, total=  44.3s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.519707, total=  50.6s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.375607, total=  48.4s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.430074, total=  47.2s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.480558, total=  45.1s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.442324, total=  49.5s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.469192, total=  46.5s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.494947, total=  49.4s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.496697, total=  52.6s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.379675, total= 1.0min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.368936, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.465290, total=  49.8s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.485518, total=  47.6s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.501855, total=  48.5s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.530527, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.500944, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.510594, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.486994, total=  47.2s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.532128, total=  55.7s
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.407630, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.509507, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.542442, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.416128, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.381576, total= 1.1min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.402203, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=5000 ...................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.389571, total= 1.3min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.359486, total= 1.3min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.356314, total= 1.2min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.382139, total= 1.3min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.458524, total= 1.4min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.353502, total=   7.8s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.350607, total= 1.1min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.399768, total=   7.9s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.404163, total= 1.2min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.414604, total= 1.2min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.346821, total=   7.7s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.402203, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.410811, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.453941, total=   8.8s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.327495, total= 1.4min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.366951, total=   7.8s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.306925, total=   8.2s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.307288, total=   8.1s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.313617, total=  11.6s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.419900, total= 1.1min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.364502, total=  10.7s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.330871, total=   8.2s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.409804, total= 1.3min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.403998, total= 1.1min
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.378507, total=   8.3s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.377859, total=   8.4s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.304857, total=   8.6s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.375165, total=   8.5s
[CV] gamma=1, kernel=rbf, C=5000 .....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.419910, total=   8.4s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.353035, total=   8.5s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.371422, total= 1.4min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.455442, total=  10.4s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.351933, total=   8.8s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.369475, total=  10.9s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.430809, total=   8.8s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.321702, total= 2.0min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.395872, total=   9.0s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.385659, total=   9.1s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.439773, total=   8.8s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.430827, total=  11.9s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.459837, total=  11.5s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ...... gamma=1, kernel=rbf, C=5000, score=0.497022, total=  12.9s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.408963, total= 2.0min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.444444, total= 1.9min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.431667, total= 1.4min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.429541, total= 1.3min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.449443, total= 1.3min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.455610, total= 1.9min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.407972, total= 1.5min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.228945, total=  58.4s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.326999, total=  58.9s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.269640, total=  55.7s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.466140, total= 1.8min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.268439, total= 1.0min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.290086, total= 1.0min
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.353804, total=  58.5s
[CV] gamma=10, kernel=rbf, C=5000 ....................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.340071, total=  59.5s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.208567, total=  58.5s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.202889, total=  58.8s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.266295, total=  56.5s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.237392, total=  59.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.296401, total=  58.2s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.326167, total=  57.7s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.513403, total= 2.1min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.325187, total=  55.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] .... gamma=0.1, kernel=rbf, C=5000, score=0.444703, total= 2.1min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.195319, total= 1.4min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.377983, total=  55.8s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.251235, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.544380, total=  16.9s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.456782, total=  20.8s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.495723, total=  17.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.506977, total=  16.6s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.441411, total= 1.3min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.255819, total= 1.1min
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.496122, total=  18.0s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.492438, total=  18.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.418253, total=  14.8s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.525109, total=  16.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.463090, total=  16.4s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.376170, total=  24.0s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.309095, total= 1.0min
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.395020, total=  18.7s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.489274, total=  17.2s
[CV] gamma=0.0001, kernel=rbf, C=10000 ...............................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.430833, total=  16.0s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.517841, total=  15.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.405787, total=  22.2s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.500220, total=  17.1s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................






    



[Parallel(n_jobs=-1)]: Done 1520 tasks      | elapsed: 35.1min






    



[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.478177, total=  16.5s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.313238, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.335096, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.469649, total=  16.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.367894, total=  57.8s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.573379, total=  21.0s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.438318, total=  16.7s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.325266, total= 1.0min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.502856, total=  17.7s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.330009, total= 1.1min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.513915, total=  17.2s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.409636, total=  15.7s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.437099, total=  20.8s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.483174, total=  20.4s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.499238, total=  19.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.494415, total=  22.0s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV]  gamma=0.0001, kernel=rbf, C=10000, score=0.461001, total=  24.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.398734, total= 1.3min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.357707, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.519119, total=  28.1s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.490173, total=  27.4s
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.422429, total=  31.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.492044, total=  25.3s
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] ..... gamma=10, kernel=rbf, C=5000, score=0.415600, total= 1.4min
[CV] gamma=0.001, kernel=rbf, C=10000 ................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.421318, total=  23.9s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.481786, total=  26.8s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.518858, total=  27.8s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.447078, total=  26.3s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.506645, total=  29.6s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.445545, total=  27.2s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.415004, total=  26.4s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.385313, total=  30.2s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.487877, total=  26.7s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.483487, total=  29.1s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.411489, total=  36.4s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.520103, total=  28.5s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.463259, total=  24.9s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.402258, total=  37.9s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.529010, total=  36.9s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.454745, total=  27.4s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.473074, total=  27.6s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.480208, total=  26.3s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.468460, total=  27.0s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.494144, total=  36.3s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.445001, total=  30.7s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.494391, total=  35.4s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.442375, total=  38.2s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] . gamma=0.001, kernel=rbf, C=10000, score=0.484736, total=  38.0s
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.374557, total= 1.2min
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.483231, total= 1.2min
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.478795, total= 1.2min
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.453642, total= 1.3min
[CV] gamma=0.01, kernel=rbf, C=10000 .................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.484268, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.376012, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.390934, total= 1.3min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.467450, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.506874, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.382148, total= 1.3min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.477813, total= 1.2min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.418523, total= 1.3min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.427580, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.471246, total= 1.3min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.494716, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.486777, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.368936, total= 1.9min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.369090, total= 1.8min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.466828, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.489165, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.513219, total= 1.4min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.532423, total= 1.9min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.502456, total= 1.9min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.469577, total= 1.3min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.501039, total= 1.8min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.522544, total= 1.5min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.534624, total= 2.0min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.393880, total= 1.9min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.348404, total= 2.0min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.399538, total= 2.0min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] .. gamma=0.01, kernel=rbf, C=10000, score=0.540163, total= 2.0min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.406751, total= 1.9min
[CV] gamma=0.1, kernel=rbf, C=10000 ..................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.392121, total= 2.1min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.378665, total= 1.9min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.359378, total= 1.8min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.354388, total=   8.9s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.399305, total=   9.0s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.350824, total= 1.8min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.352601, total=   9.0s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.400274, total= 1.8min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.400979, total=  10.6s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.412926, total=   9.4s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.419554, total= 2.0min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.437214, total= 2.4min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.451192, total=   9.8s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.314894, total=  12.1s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.382609, total= 2.5min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.365076, total=  10.9s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.310790, total=  10.5s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.303805, total=  11.3s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.330027, total= 2.4min
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.383457, total=  10.2s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.302472, total=  10.0s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.329605, total=  13.1s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.378088, total=  11.8s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.358504, total=  16.5s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.453166, total=  15.2s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.375826, total=  11.3s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.355545, total=   9.9s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.420555, total=  11.7s
[CV] gamma=1, kernel=rbf, C=10000 ....................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.358524, total=   9.7s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.374386, total=  12.4s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.410771, total= 2.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.430594, total=  11.2s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.402829, total=  12.8s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.433319, total=  14.5s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.382651, total= 2.5min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.387695, total=  11.1s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.412169, total= 2.4min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.459061, total=  12.9s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.442169, total=  11.7s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ..... gamma=1, kernel=rbf, C=10000, score=0.486969, total=  14.5s
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.311489, total= 3.4min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.400483, total= 2.3min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.426732, total= 2.3min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.410021, total= 3.5min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.409183, total= 2.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.450133, total= 3.5min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.420455, total= 2.2min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.228280, total= 1.0min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.326999, total= 1.0min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.289841, total= 1.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.268671, total= 1.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.269640, total= 1.0min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.340071, total= 1.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.353804, total= 1.1min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.202889, total= 1.0min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.441254, total= 3.3min
[CV] gamma=10, kernel=rbf, C=10000 ...................................
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.237392, total= 1.0min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.208359, total= 1.1min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.266089, total=  58.4s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.296401, total= 1.0min
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.410368, total= 2.6min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.195319, total= 1.5min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.326167, total=  58.1s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.250529, total= 1.4min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.325187, total=  52.4s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.377983, total=  53.8s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.256047, total=  56.9s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.309534, total=  52.7s
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.452846, total= 3.3min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.441411, total= 1.3min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.367894, total=  46.5s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.329777, total=  51.5s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.313238, total=  54.7s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.325266, total=  51.4s
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.335096, total= 1.1min
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.443927, total= 3.6min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.398734, total=  59.7s
[CV] ... gamma=0.1, kernel=rbf, C=10000, score=0.506329, total= 3.6min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.357707, total= 1.2min
[CV] .... gamma=10, kernel=rbf, C=10000, score=0.415600, total= 1.1min






    



[Parallel(n_jobs=-1)]: Done 1680 out of 1680 | elapsed: 43.1min finished

Let's plot the find the best results



In [26]:

    
pd.DataFrame(clf.cv_results_).sort_values(by="rank_test_score").head()









    Out[26]:






  
    
      
      mean_fit_time
      mean_score_time
      mean_test_score
      mean_train_score
      param_C
      param_gamma
      param_kernel
      params
      rank_test_score
      split0_test_score
      ...
      split7_test_score
      split7_train_score
      split8_test_score
      split8_train_score
      split9_test_score
      split9_train_score
      std_fit_time
      std_score_time
      std_test_score
      std_train_score
    
  
  
    
      14
      7.158785
      3.548661
      0.476873
      0.611961
      5
      0.01
      rbf
      {'gamma': 0.01, 'kernel': 'rbf', 'C': 5}
      1
      0.455230
      ...
      0.526360
      0.600548
      0.468289
      0.626182
      0.430072
      0.639314
      1.146031
      0.519848
      0.039089
      0.012052
    
    
      54
      15.052618
      3.320419
      0.475171
      0.593774
      10000
      0.0001
      rbf
      {'gamma': 0.0001, 'kernel': 'rbf', 'C': 10000}
      2
      0.456782
      ...
      0.525109
      0.584474
      0.463090
      0.607973
      0.418253
      0.619926
      2.938377
      0.467400
      0.043729
      0.012762
    
    
      37
      8.427462
      3.485163
      0.474773
      0.592844
      100
      0.001
      rbf
      {'gamma': 0.001, 'kernel': 'rbf', 'C': 100}
      3
      0.450798
      ...
      0.528860
      0.583591
      0.462258
      0.607531
      0.422412
      0.619233
      1.491136
      0.485869
      0.044061
      0.013014
    
    
      20
      7.136548
      3.431708
      0.474418
      0.621774
      10
      0.01
      rbf
      {'gamma': 0.01, 'kernel': 'rbf', 'C': 10}
      4
      0.450576
      ...
      0.526776
      0.611499
      0.468289
      0.632193
      0.430948
      0.644681
      1.133364
      0.523610
      0.039578
      0.011432
    
    
      43
      11.131641
      3.298738
      0.473763
      0.617477
      1000
      0.001
      rbf
      {'gamma': 0.001, 'kernel': 'rbf', 'C': 1000}
      5
      0.453901
      ...
      0.526360
      0.602049
      0.472240
      0.627243
      0.427884
      0.640007
      2.080823
      0.503587
      0.039613
      0.011837
    
  

5 rows × 69 columns

C = 5 and gamma = 0.01 seem to give the best F1 score. Let's try using these against the test dataset



In [44]:

    
clf_svm = SVC(C=5, gamma=0.01)
clf_svm.fit(X_train, y_train)
predicted_labels = clf_svm.predict(X_test)

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS    73    59     2                 1                     135
     CSiS    18   251    69     3           1     1               343
     FSiS    10   100   174     4           1                     289
     SiSh           2     6    69          28           1         106
       MS           2     1    15          81     7     4         110
       WS           1     1    15     2   161     5    43     1   229
        D                 1     6     4    22    28    20     1    82
       PS           1     1     8     2    55     5   139    17   228
       BS                                   7     2    23    59    91

Precision  0.72  0.60  0.68  0.57  0.00  0.45  0.58  0.60  0.76  0.57
   Recall  0.54  0.73  0.60  0.65  0.00  0.70  0.34  0.61  0.65  0.59
       F1  0.62  0.66  0.64  0.61  0.00  0.55  0.43  0.61  0.70  0.57

DecisionTree classifier

Decision trees are very powerful for high entropy datasets however they have an tendancy to overfit. One way of controlling this is by limiting the depth of the tree using the max_depth parameter. We can use GridSearchCV for this too.



In [28]:

    
from sklearn import tree

parameters = {'max_depth': np.arange(2, 35)}
clf_dt = tree.DecisionTreeClassifier()
clf = LPWO_CV(clf_dt, parameters)









    



Fitting 28 folds for each of 33 candidates, totalling 924 fits
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.344637, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.431054, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.429595, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.367568, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.455095, total=   0.0s
[CV] ...................... max_depth=2, score=0.339149, total=   0.0s
[CV] ...................... max_depth=2, score=0.410771, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.390009, total=   0.0s
[CV] ...................... max_depth=2, score=0.381368, total=   0.0s
[CV] ...................... max_depth=2, score=0.218210, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.400928, total=   0.0s
[CV] ...................... max_depth=2, score=0.275935, total=   0.0s
[CV] ...................... max_depth=2, score=0.370050, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.398742, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.359561, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.417141, total=   0.0s
[CV] ...................... max_depth=2, score=0.427565, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.340572, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.438384, total=   0.0s
[CV] ...................... max_depth=2, score=0.397431, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=2, score=0.399385, total=   0.0s
[CV] max_depth=2 .....................................................
[CV] ...................... max_depth=2, score=0.380141, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=2, score=0.285682, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=2, score=0.464278, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=2, score=0.407931, total=   0.0s
[CV] ...................... max_depth=2, score=0.352197, total=   0.0s
[CV] ...................... max_depth=2, score=0.375953, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=2, score=0.315095, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.337323, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.488297, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.390520, total=   0.0s
[CV] ...................... max_depth=3, score=0.425949, total=   0.0s
[CV] ...................... max_depth=3, score=0.450733, total=   0.0s
[CV] ...................... max_depth=3, score=0.311915, total=   0.0s
[CV] ...................... max_depth=3, score=0.450294, total=   0.1s
[CV] ...................... max_depth=3, score=0.452386, total=   0.0s
[CV] ...................... max_depth=3, score=0.391765, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.270995, total=   0.0s
[CV] ...................... max_depth=3, score=0.316043, total=   0.0s
[CV] ...................... max_depth=3, score=0.371386, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.417904, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.474198, total=   0.0s
[CV] max_depth=4 .....................................................
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=3, score=0.520586, total=   0.0s
[CV] ...................... max_depth=3, score=0.556583, total=   0.0s
[CV] ...................... max_depth=3, score=0.482656, total=   0.0s
[CV] ...................... max_depth=3, score=0.505119, total=   0.0s
[CV] max_depth=4 .....................................................
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=3, score=0.408729, total=   0.0s
[CV] ...................... max_depth=3, score=0.355497, total=   0.0s
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=3, score=0.445272, total=   0.0s
[CV] max_depth=5 .....................................................
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=3, score=0.474956, total=   0.0s
[CV] max_depth=3 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=4, score=0.346860, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.480625, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=6 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.342128, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.382979, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=3, score=0.482466, total=   0.0s
[CV] ...................... max_depth=3, score=0.409140, total=   0.0s
[CV] ...................... max_depth=4, score=0.500208, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=5, score=0.505729, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=5, score=0.450495, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=3 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=4, score=0.393034, total=   0.1s
[CV] ...................... max_depth=5, score=0.407374, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=5, score=0.512780, total=   0.1s
[CV] ...................... max_depth=4, score=0.353239, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=6, score=0.421501, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.477086, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=4, score=0.463590, total=   0.1s
[CV] ...................... max_depth=6, score=0.412948, total=   0.1s
[CV] ...................... max_depth=4, score=0.393617, total=   0.1s
[CV] ...................... max_depth=5, score=0.506605, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=6, score=0.436072, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=5, score=0.495103, total=   0.1s
[CV] ...................... max_depth=6, score=0.461574, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=3, score=0.415455, total=   0.1s
[CV] max_depth=3 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=6, score=0.542442, total=   0.1s
[CV] ...................... max_depth=7, score=0.371064, total=   0.1s
[CV] ...................... max_depth=7, score=0.376853, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=4, score=0.469192, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=7, score=0.462351, total=   0.1s
[CV] ...................... max_depth=5, score=0.405898, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=7 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=7, score=0.486498, total=   0.1s
[CV] ...................... max_depth=5, score=0.459766, total=   0.1s
[CV] ...................... max_depth=5, score=0.433062, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=8, score=0.509166, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=3, score=0.492115, total=   0.1s
[CV] ...................... max_depth=4, score=0.426774, total=   0.1s
[CV] max_depth=3 .....................................................
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=8, score=0.409353, total=   0.2s
[CV] ...................... max_depth=8, score=0.435189, total=   0.1s
[CV] ...................... max_depth=8, score=0.389026, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=6, score=0.346465, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=4, score=0.450577, total=   0.1s
[CV] ...................... max_depth=5, score=0.411098, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.490147, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=5, score=0.438553, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=6, score=0.485947, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.549710, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=4, score=0.394409, total=   0.1s
[CV] ...................... max_depth=5, score=0.504117, total=   0.1s
[CV] ...................... max_depth=6, score=0.490089, total=   0.1s
[CV] ...................... max_depth=7, score=0.332349, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=6, score=0.497535, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=3, score=0.406017, total=   0.1s
[CV] max_depth=3 .....................................................
[CV] ...................... max_depth=6, score=0.442595, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.486380, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=4, score=0.449696, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=5, score=0.501862, total=   0.1s
[CV] ...................... max_depth=7, score=0.510213, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.394178, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=4, score=0.465930, total=   0.1s
[CV] ...................... max_depth=7, score=0.484371, total=   0.1s
[CV] ...................... max_depth=5, score=0.485924, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=4, score=0.400231, total=   0.1s
[CV] ...................... max_depth=4, score=0.454404, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=7, score=0.448284, total=   0.1s
[CV] ...................... max_depth=8, score=0.461346, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=3, score=0.446643, total=   0.1s
[CV] ...................... max_depth=8, score=0.427147, total=   0.1s
[CV] ...................... max_depth=6, score=0.473203, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=4, score=0.474517, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=8, score=0.463731, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=6, score=0.472039, total=   0.1s
[CV] ...................... max_depth=6, score=0.401553, total=   0.2s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=8, score=0.436343, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=7, score=0.403672, total=   0.1s
[CV] ...................... max_depth=6, score=0.381277, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=7, score=0.419548, total=   0.1s
[CV] ...................... max_depth=5, score=0.431452, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.524839, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.454367, total=   0.1s
[CV] ...................... max_depth=7, score=0.459151, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=4, score=0.372427, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=5, score=0.370638, total=   0.1s
[CV] ...................... max_depth=5, score=0.407904, total=   0.1s
[CV] max_depth=5 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=4, score=0.508563, total=   0.1s
[CV] max_depth=4 .....................................................
[CV] max_depth=5 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.428234, total=   0.1s
[CV] ...................... max_depth=7, score=0.442886, total=   0.1s
[CV] ...................... max_depth=4, score=0.514867, total=   0.1s
[CV] ...................... max_depth=6, score=0.449212, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] max_depth=4 .....................................................
[CV] ...................... max_depth=7, score=0.483960, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=6 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=8, score=0.471866, total=   0.1s
[CV] ...................... max_depth=6, score=0.492582, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=8, score=0.398836, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=4, score=0.458288, total=   0.1s
[CV] ...................... max_depth=6, score=0.319899, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=8, score=0.424338, total=   0.1s
[CV] ...................... max_depth=8, score=0.444855, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=4, score=0.465116, total=   0.1s
[CV] ...................... max_depth=7, score=0.462109, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=5, score=0.484606, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] max_depth=7 .....................................................
[CV] max_depth=8 .....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=5, score=0.352606, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=7, score=0.450781, total=   0.1s
[CV] ...................... max_depth=6, score=0.506228, total=   0.1s
[CV] ...................... max_depth=6, score=0.414229, total=   0.1s
[CV] ...................... max_depth=7, score=0.457499, total=   0.1s
[CV] ...................... max_depth=4, score=0.394397, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=6 .....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=5, score=0.495817, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=6, score=0.475232, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=4, score=0.577550, total=   0.1s
[CV] ...................... max_depth=7, score=0.466972, total=   0.1s
[CV] ...................... max_depth=5, score=0.462817, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=8, score=0.439807, total=   0.2s
[CV] max_depth=5 .....................................................
[CV] ...................... max_depth=6, score=0.388614, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=7, score=0.447808, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=10 ....................................................
[CV] ...................... max_depth=6, score=0.461433, total=   0.1s
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=8, score=0.344496, total=   0.1s
[CV] ...................... max_depth=9, score=0.449711, total=   0.1s
[CV] ...................... max_depth=6, score=0.519351, total=   0.1s
[CV] ...................... max_depth=8, score=0.536974, total=   0.1s
[CV] max_depth=10 ....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=8, score=0.445777, total=   0.1s
[CV] ...................... max_depth=7, score=0.451011, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=7, score=0.432601, total=   0.1s
[CV] ...................... max_depth=8, score=0.486224, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=6, score=0.520165, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=6 .....................................................
[CV] ...................... max_depth=9, score=0.401539, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=9, score=0.460887, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=5, score=0.488927, total=   0.1s
[CV] ...................... max_depth=7, score=0.484675, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.457162, total=   0.2s
[CV] ...................... max_depth=7, score=0.405906, total=   0.1s
[CV] ...................... max_depth=6, score=0.407839, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=6, score=0.421075, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] max_depth=10 ....................................................
[CV] ...................... max_depth=5, score=0.465909, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.486225, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] max_depth=11 ....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=7, score=0.462770, total=   0.1s
[CV] ...................... max_depth=6, score=0.422743, total=   0.1s
[CV] ...................... max_depth=8, score=0.447329, total=   0.1s
[CV] max_depth=7 .....................................................
[CV] ...................... max_depth=8, score=0.383910, total=   0.1s
[CV] ..................... max_depth=10, score=0.348511, total=   0.2s
[CV] ...................... max_depth=7, score=0.459486, total=   0.1s
[CV] ...................... max_depth=6, score=0.451969, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=8, score=0.461030, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=8 .....................................................
[CV] max_depth=11 ....................................................
[CV] max_depth=10 ....................................................
[CV] ...................... max_depth=7, score=0.487234, total=   0.1s
[CV] max_depth=8 .....................................................
[CV] ..................... max_depth=10, score=0.457508, total=   0.2s
[CV] ..................... max_depth=10, score=0.437191, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=12 ....................................................
[CV] max_depth=8 .....................................................
[CV] ...................... max_depth=8, score=0.382553, total=   0.2s
[CV] ...................... max_depth=7, score=0.399650, total=   0.1s
[CV] max_depth=10 ....................................................
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=8, score=0.426657, total=   0.1s
[CV] ...................... max_depth=9, score=0.464382, total=   0.2s
[CV] ...................... max_depth=7, score=0.549109, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] max_depth=8 .....................................................
[CV] max_depth=12 ....................................................
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=9, score=0.522184, total=   0.2s
[CV] ...................... max_depth=9, score=0.343839, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=11, score=0.412677, total=   0.2s
[CV] ..................... max_depth=11, score=0.428218, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=7, score=0.513178, total=   0.1s
[CV] ...................... max_depth=9, score=0.467436, total=   0.1s
[CV] ..................... max_depth=10, score=0.501454, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=9, score=0.431882, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=11, score=0.470898, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=8, score=0.483174, total=   0.1s
[CV] max_depth=12 ....................................................
[CV] max_depth=8 .....................................................
[CV] max_depth=10 ....................................................
[CV] ...................... max_depth=8, score=0.355138, total=   0.1s
[CV] ..................... max_depth=11, score=0.360338, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=11, score=0.414386, total=   0.1s
[CV] ..................... max_depth=10, score=0.482021, total=   0.2s
[CV] ...................... max_depth=8, score=0.495652, total=   0.1s
[CV] ..................... max_depth=10, score=0.350707, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=8, score=0.496237, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=12, score=0.389062, total=   0.1s
[CV] ...................... max_depth=8, score=0.489654, total=   0.1s
[CV] ..................... max_depth=10, score=0.476672, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=12, score=0.452486, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=9, score=0.369787, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.475782, total=   0.1s
[CV] ..................... max_depth=12, score=0.436072, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=12, score=0.421546, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=8, score=0.447588, total=   0.1s
[CV] ..................... max_depth=10, score=0.393839, total=   0.1s
[CV] ...................... max_depth=9, score=0.496053, total=   0.2s
[CV] ..................... max_depth=11, score=0.456137, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=9, score=0.395907, total=   0.2s
[CV] ..................... max_depth=11, score=0.420859, total=   0.1s
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=10, score=0.414889, total=   0.1s
[CV] ..................... max_depth=11, score=0.458401, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] max_depth=11 ....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=10, score=0.419967, total=   0.1s
[CV] max_depth=10 ....................................................
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=10, score=0.467003, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=12, score=0.484736, total=   0.2s
[CV] ..................... max_depth=13, score=0.326809, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=11, score=0.458673, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=9, score=0.395390, total=   0.1s
[CV] ..................... max_depth=10, score=0.407576, total=   0.1s
[CV] ..................... max_depth=12, score=0.417136, total=   0.1s
[CV] ...................... max_depth=9, score=0.485311, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=11, score=0.435965, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=11 ....................................................
[CV] max_depth=9 .....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=12, score=0.317794, total=   0.2s
[CV] ...................... max_depth=9, score=0.500108, total=   0.1s
[CV] max_depth=12 ....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=13, score=0.418098, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=11, score=0.396964, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=10, score=0.416422, total=   0.1s
[CV] ..................... max_depth=10, score=0.459096, total=   0.1s
[CV] max_depth=10 ....................................................
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=13, score=0.476527, total=   0.2s
[CV] ..................... max_depth=11, score=0.459653, total=   0.2s
[CV] ..................... max_depth=12, score=0.492605, total=   0.2s
[CV] ..................... max_depth=10, score=0.472807, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=12 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=10 ....................................................
[CV] ...................... max_depth=9, score=0.372863, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.441790, total=   0.2s
[CV] ..................... max_depth=12, score=0.397554, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=11, score=0.420631, total=   0.2s
[CV] ..................... max_depth=10, score=0.411752, total=   0.2s
[CV] ..................... max_depth=13, score=0.438073, total=   0.2s
[CV] ..................... max_depth=13, score=0.433310, total=   0.3s
[CV] ..................... max_depth=12, score=0.464605, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=13 ....................................................
[CV] ...................... max_depth=9, score=0.463036, total=   0.1s
[CV] ..................... max_depth=11, score=0.446837, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=11, score=0.491437, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=14 ....................................................
[CV] ...................... max_depth=9, score=0.470669, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=10, score=0.446165, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=12, score=0.337872, total=   0.2s
[CV] ..................... max_depth=11, score=0.334647, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=13, score=0.468071, total=   0.2s
[CV] ..................... max_depth=10, score=0.461272, total=   0.1s
[CV] ..................... max_depth=12, score=0.431546, total=   0.2s
[CV] ..................... max_depth=10, score=0.450136, total=   0.1s
[CV] ...................... max_depth=9, score=0.432756, total=   0.1s
[CV] max_depth=10 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=11, score=0.438923, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=10, score=0.401539, total=   0.1s
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.407805, total=   0.2s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=12, score=0.431088, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=9 .....................................................
[CV] ...................... max_depth=9, score=0.435874, total=   0.1s
[CV] ..................... max_depth=11, score=0.499052, total=   0.2s
[CV] ..................... max_depth=13, score=0.468378, total=   0.2s
[CV] ..................... max_depth=13, score=0.408245, total=   0.2s
[CV] max_depth=9 .....................................................
[CV] ..................... max_depth=11, score=0.405278, total=   0.2s
[CV] ..................... max_depth=11, score=0.439991, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=10, score=0.460018, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ...................... max_depth=9, score=0.447385, total=   0.1s
[CV] ..................... max_depth=13, score=0.337413, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] ..................... max_depth=14, score=0.395833, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=13, score=0.415584, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=12, score=0.497300, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=12, score=0.457109, total=   0.2s
[CV] ..................... max_depth=10, score=0.445791, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ...................... max_depth=9, score=0.450781, total=   0.1s
[CV] ..................... max_depth=10, score=0.430355, total=   0.1s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=12, score=0.347753, total=   0.2s
[CV] max_depth=10 ....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=11, score=0.439308, total=   0.2s
[CV] ..................... max_depth=13, score=0.392743, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=10, score=0.342088, total=   0.2s
[CV] ..................... max_depth=10, score=0.493671, total=   0.2s
[CV] ..................... max_depth=12, score=0.476242, total=   0.2s
[CV] ...................... max_depth=9, score=0.449188, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=11 ....................................................
[CV] max_depth=10 ....................................................
[CV] max_depth=15 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=11, score=0.356596, total=   0.2s
[CV] ..................... max_depth=13, score=0.440925, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=11, score=0.433950, total=   0.1s
[CV] ..................... max_depth=10, score=0.436602, total=   0.1s
[CV] max_depth=11 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=12, score=0.400931, total=   0.2s
[CV] ..................... max_depth=13, score=0.453302, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ...................... max_depth=9, score=0.471867, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=11, score=0.480681, total=   0.2s
[CV] ..................... max_depth=13, score=0.435850, total=   0.2s
[CV] ..................... max_depth=13, score=0.390183, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=15 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=12 ....................................................
[CV] max_depth=11 ....................................................
[CV] ..................... max_depth=14, score=0.449826, total=   0.2s
[CV] ..................... max_depth=14, score=0.473877, total=   0.2s
[CV] ..................... max_depth=12, score=0.424629, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] max_depth=14 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=12, score=0.486709, total=   0.2s
[CV] ..................... max_depth=10, score=0.507774, total=   0.2s
[CV] ..................... max_depth=12, score=0.431106, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=11, score=0.341000, total=   0.1s
[CV] max_depth=15 ....................................................
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=10, score=0.453714, total=   0.1s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=14, score=0.388826, total=   0.2s
[CV] ..................... max_depth=12, score=0.391146, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=11, score=0.484197, total=   0.1s
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=14, score=0.417904, total=   0.2s
[CV] ..................... max_depth=14, score=0.383843, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=13, score=0.479411, total=   0.2s
[CV] ..................... max_depth=11, score=0.474501, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=13, score=0.410272, total=   0.2s
[CV] ..................... max_depth=15, score=0.390310, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=16 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=12, score=0.462109, total=   0.1s
[CV] ..................... max_depth=13, score=0.379011, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=11, score=0.420455, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=15, score=0.418035, total=   0.2s
[CV] max_depth=15 ....................................................






    



[Parallel(n_jobs=-1)]: Done 240 tasks      | elapsed:    2.2s






    



[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=13, score=0.433988, total=   0.2s
[CV] ..................... max_depth=12, score=0.380457, total=   0.2s
[CV] max_depth=12 ....................................................
[CV] ..................... max_depth=13, score=0.451300, total=   0.2s
[CV] ..................... max_depth=14, score=0.438216, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=15, score=0.397072, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=14, score=0.436301, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=12, score=0.465930, total=   0.2s
[CV] ..................... max_depth=15, score=0.407293, total=   0.2s
[CV] ..................... max_depth=12, score=0.411969, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=17 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=15, score=0.464631, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=14, score=0.354944, total=   0.2s
[CV] ..................... max_depth=12, score=0.431810, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=16, score=0.410172, total=   0.2s
[CV] ..................... max_depth=13, score=0.326330, total=   0.2s
[CV] ..................... max_depth=14, score=0.417540, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=16, score=0.320426, total=   0.2s
[CV] ..................... max_depth=16, score=0.417784, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=15, score=0.320420, total=   0.2s
[CV] ..................... max_depth=13, score=0.412397, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=15, score=0.392411, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=13 ....................................................
[CV] ..................... max_depth=14, score=0.391764, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=13, score=0.429921, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=13 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=12, score=0.439659, total=   0.2s
[CV] ..................... max_depth=14, score=0.396756, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=17, score=0.386525, total=   0.2s
[CV] ..................... max_depth=16, score=0.481097, total=   0.2s
[CV] ..................... max_depth=14, score=0.411750, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=13, score=0.406365, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=13, score=0.489576, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=15, score=0.478195, total=   0.2s
[CV] max_depth=13 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=15, score=0.395339, total=   0.2s
[CV] ..................... max_depth=15, score=0.378674, total=   0.2s
[CV] ..................... max_depth=16, score=0.459901, total=   0.2s
[CV] ..................... max_depth=17, score=0.400371, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=17, score=0.464482, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=15 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=14, score=0.436951, total=   0.2s
[CV] ..................... max_depth=16, score=0.334037, total=   0.2s
[CV] ..................... max_depth=17, score=0.358969, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=14, score=0.397301, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=16, score=0.442068, total=   0.2s
[CV] ..................... max_depth=17, score=0.387494, total=   0.2s
[CV] ..................... max_depth=13, score=0.402489, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=13, score=0.490330, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=17 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=15, score=0.315745, total=   0.2s
[CV] ..................... max_depth=16, score=0.412801, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=15 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=14, score=0.330050, total=   0.2s
[CV] ..................... max_depth=17, score=0.417845, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=15, score=0.429428, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=18, score=0.409017, total=   0.2s
[CV] ..................... max_depth=15, score=0.410832, total=   0.2s
[CV] ..................... max_depth=18, score=0.387399, total=   0.2s
[CV] ..................... max_depth=14, score=0.456441, total=   0.3s
[CV] max_depth=18 ....................................................
[CV] max_depth=14 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=16, score=0.394282, total=   0.1s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=14, score=0.314894, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=13, score=0.441298, total=   0.2s
[CV] ..................... max_depth=17, score=0.448427, total=   0.2s
[CV] ..................... max_depth=17, score=0.371639, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=15, score=0.484005, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=14, score=0.396911, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=16, score=0.478918, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=16, score=0.361707, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=17, score=0.405898, total=   0.2s
[CV] ..................... max_depth=16, score=0.417492, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=14, score=0.478953, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=18, score=0.379918, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=15, score=0.432667, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=17, score=0.390253, total=   0.2s
[CV] ..................... max_depth=18, score=0.387522, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=17, score=0.414566, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=14, score=0.442484, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=15, score=0.322431, total=   0.2s
[CV] ..................... max_depth=16, score=0.353992, total=   0.2s
[CV] ..................... max_depth=18, score=0.332239, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=18, score=0.393635, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=18 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=14, score=0.407319, total=   0.2s
[CV] ..................... max_depth=16, score=0.424565, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=15, score=0.431818, total=   0.2s
[CV] ..................... max_depth=14, score=0.449119, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=15, score=0.459901, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=16, score=0.433840, total=   0.2s
[CV] ..................... max_depth=18, score=0.451601, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=17, score=0.375114, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=17, score=0.404034, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=16, score=0.403853, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=16, score=0.381396, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=14, score=0.467387, total=   0.2s
[CV] ..................... max_depth=17, score=0.463142, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=18, score=0.473644, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=14, score=0.340156, total=   0.2s
[CV] ..................... max_depth=14, score=0.414795, total=   0.2s
[CV] max_depth=14 ....................................................
[CV] ..................... max_depth=18, score=0.381463, total=   0.2s
[CV] ..................... max_depth=15, score=0.479148, total=   0.2s
[CV] ..................... max_depth=17, score=0.387302, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=19 ....................................................
[CV] max_depth=17 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=17, score=0.393880, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=15, score=0.409447, total=   0.2s
[CV] ..................... max_depth=16, score=0.420347, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=15 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=18, score=0.317021, total=   0.2s
[CV] ..................... max_depth=15, score=0.377881, total=   0.2s
[CV] ..................... max_depth=18, score=0.411080, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=16, score=0.409003, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=18, score=0.402091, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=16, score=0.400083, total=   0.2s
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=15, score=0.369010, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=16, score=0.416301, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=17, score=0.321733, total=   0.2s
[CV] ..................... max_depth=15, score=0.392898, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=15 ....................................................
[CV] ..................... max_depth=16, score=0.389067, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=17, score=0.447478, total=   0.2s
[CV] ..................... max_depth=19, score=0.311064, total=   0.3s
[CV] max_depth=19 ....................................................
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=18, score=0.411273, total=   0.2s
[CV] ..................... max_depth=15, score=0.440925, total=   0.2s
[CV] ..................... max_depth=14, score=0.470651, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=17, score=0.387470, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=17, score=0.406883, total=   0.2s
[CV] ..................... max_depth=14, score=0.425325, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=16, score=0.389963, total=   0.2s
[CV] ..................... max_depth=15, score=0.374024, total=   0.2s
[CV] ..................... max_depth=19, score=0.418137, total=   0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=18, score=0.469880, total=   0.2s
[CV] max_depth=20 ....................................................
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=17, score=0.316170, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=18, score=0.439718, total=   0.2s
[CV] ..................... max_depth=16, score=0.321296, total=   0.2s
[CV] ..................... max_depth=15, score=0.437543, total=   0.2s
[CV] ..................... max_depth=18, score=0.326018, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=18 ....................................................
[CV] max_depth=18 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=16, score=0.472077, total=   0.2s
[CV] max_depth=16 ....................................................
[CV] ..................... max_depth=15, score=0.465433, total=   0.2s
[CV] ..................... max_depth=19, score=0.380541, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=17, score=0.415667, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=15, score=0.419868, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=19, score=0.417657, total=   0.2s
[CV] ..................... max_depth=16, score=0.386183, total=   0.2s
[CV] ..................... max_depth=18, score=0.444206, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=16, score=0.467577, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=17, score=0.406429, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] ..................... max_depth=19, score=0.440798, total=   0.2s
[CV] ..................... max_depth=20, score=0.393839, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=21 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=18, score=0.391179, total=   0.2s
[CV] ..................... max_depth=19, score=0.439953, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=20, score=0.401609, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=19, score=0.334459, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=17, score=0.439248, total=   0.2s
[CV] ..................... max_depth=18, score=0.406353, total=   0.2s
[CV] max_depth=17 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=17, score=0.466556, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=20, score=0.365528, total=   0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=18, score=0.477773, total=   0.2s
[CV] ..................... max_depth=16, score=0.399695, total=   0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=20, score=0.349840, total=   0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=19, score=0.453666, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=18, score=0.415353, total=   0.2s
[CV] ..................... max_depth=19, score=0.430127, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=21, score=0.392805, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=21, score=0.401850, total=   0.2s
[CV] ..................... max_depth=21, score=0.374657, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=20, score=0.474106, total=   0.3s
[CV] max_depth=20 ....................................................
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=17, score=0.465276, total=   0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=20, score=0.428737, total=   0.2s
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[CV] ..................... max_depth=19, score=0.414398, total=   0.2s
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[CV] max_depth=19 ....................................................
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[CV] max_depth=18 ....................................................
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[CV] ..................... max_depth=18, score=0.434882, total=   0.2s
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[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=17, score=0.409555, total=   0.2s
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[CV] ..................... max_depth=21, score=0.327205, total=   0.2s
[CV] max_depth=21 ....................................................
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[CV] max_depth=18 ....................................................
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[CV] max_depth=19 ....................................................
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[CV] max_depth=19 ....................................................
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[CV] max_depth=20 ....................................................
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[CV] max_depth=21 ....................................................
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[CV] max_depth=20 ....................................................
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[CV] max_depth=19 ....................................................
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[CV] ..................... max_depth=22, score=0.456170, total=   0.2s
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[CV] ..................... max_depth=18, score=0.412883, total=   0.2s
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[CV] max_depth=22 ....................................................
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[CV] ..................... max_depth=22, score=0.410167, total=   0.2s
[CV] max_depth=22 ....................................................
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[CV] ..................... max_depth=18, score=0.402720, total=   0.3s
[CV] ..................... max_depth=19, score=0.390631, total=   0.2s
[CV] ..................... max_depth=20, score=0.386863, total=   0.2s
[CV] max_depth=19 ....................................................
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[CV] ..................... max_depth=19, score=0.431898, total=   0.2s
[CV] max_depth=19 ....................................................
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[CV] max_depth=19 ....................................................
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[CV] max_depth=20 ....................................................
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[CV] ..................... max_depth=19, score=0.376258, total=   0.2s
[CV] max_depth=19 ....................................................
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[CV] ..................... max_depth=22, score=0.419991, total=   0.2s
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[CV] max_depth=22 ....................................................
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[CV] max_depth=23 ....................................................
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[CV] max_depth=21 ....................................................
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[CV] ..................... max_depth=23, score=0.316699, total=   0.2s
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[CV] ..................... max_depth=19, score=0.405097, total=   0.2s
[CV] max_depth=23 ....................................................
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[CV] max_depth=23 ....................................................
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[CV] ..................... max_depth=23, score=0.431750, total=   0.2s
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[CV] max_depth=22 ....................................................
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[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=19, score=0.313854, total=   0.2s
[CV] ..................... max_depth=20, score=0.318450, total=   0.2s
[CV] max_depth=20 ....................................................
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[CV] ..................... max_depth=22, score=0.420586, total=   0.2s
[CV] ..................... max_depth=21, score=0.450353, total=   0.3s
[CV] max_depth=23 ....................................................
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[CV] ..................... max_depth=19, score=0.459047, total=   0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=21, score=0.442700, total=   0.2s
[CV] ..................... max_depth=19, score=0.461509, total=   0.2s
[CV] ..................... max_depth=22, score=0.438425, total=   0.1s
[CV] ..................... max_depth=22, score=0.366433, total=   0.2s
[CV] ..................... max_depth=22, score=0.383609, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=22 ....................................................
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[CV] ..................... max_depth=21, score=0.435253, total=   0.2s
[CV] max_depth=21 ....................................................
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[CV] ..................... max_depth=22, score=0.433835, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=19, score=0.389831, total=   0.2s
[CV] max_depth=22 ....................................................
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[CV] max_depth=23 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=23, score=0.426756, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=22, score=0.387279, total=   0.2s
[CV] ..................... max_depth=23, score=0.425900, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=20, score=0.394903, total=   0.3s
[CV] ..................... max_depth=22, score=0.362163, total=   0.2s
[CV] ..................... max_depth=20, score=0.320000, total=   0.3s
[CV] max_depth=21 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=20, score=0.475280, total=   0.2s
[CV] ..................... max_depth=20, score=0.416997, total=   0.2s
[CV] ..................... max_depth=23, score=0.414566, total=   0.2s
[CV] max_depth=20 ....................................................
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[CV] ..................... max_depth=20, score=0.422371, total=   0.2s
[CV] max_depth=20 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.407178, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=19, score=0.399913, total=   0.2s
[CV] ..................... max_depth=21, score=0.414398, total=   0.3s
[CV] max_depth=21 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.375027, total=   0.1s
[CV] ..................... max_depth=23, score=0.343907, total=   0.2s
[CV] ..................... max_depth=21, score=0.400974, total=   0.2s
[CV] ..................... max_depth=22, score=0.387815, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=22, score=0.412023, total=   0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.358284, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=21 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=21, score=0.461503, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=23, score=0.360552, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=23, score=0.437368, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.324752, total=   0.2s
[CV] ..................... max_depth=23, score=0.397735, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.394885, total=   0.2s
[CV] ..................... max_depth=22, score=0.369037, total=   0.3s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.395833, total=   0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.471369, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=20, score=0.449151, total=   0.2s
[CV] ..................... max_depth=20, score=0.400278, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=20, score=0.324541, total=   0.2s
[CV] ..................... max_depth=24, score=0.410636, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.388646, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.454289, total=   0.3s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=21, score=0.377277, total=   0.2s
[CV] ..................... max_depth=20, score=0.439718, total=   0.3s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=23, score=0.433319, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=22, score=0.388005, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=23, score=0.370989, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=21, score=0.386475, total=   0.2s
[CV] ..................... max_depth=24, score=0.372827, total=   0.2s
[CV] ..................... max_depth=23, score=0.393275, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] max_depth=24 ....................................................
[CV] max_depth=25 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=24, score=0.384530, total=   0.2s
[CV] ..................... max_depth=24, score=0.341844, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.415211, total=   0.2s
[CV] ..................... max_depth=22, score=0.319545, total=   0.2s
[CV] ..................... max_depth=21, score=0.438633, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=21, score=0.427810, total=   0.2s
[CV] ..................... max_depth=21, score=0.365754, total=   0.3s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=22, score=0.378545, total=   0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=23, score=0.408851, total=   0.2s
[CV] max_depth=23 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=24, score=0.458611, total=   0.2s
[CV] ..................... max_depth=24, score=0.404438, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.390942, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.326330, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.454858, total=   0.2s
[CV] ..................... max_depth=25, score=0.425029, total=   0.2s
[CV] ..................... max_depth=24, score=0.407004, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.416512, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=24, score=0.420792, total=   0.2s
[CV] ..................... max_depth=24, score=0.443410, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.370311, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.394112, total=   0.2s
[CV] ..................... max_depth=24, score=0.420857, total=   0.2s
[CV] ..................... max_depth=25, score=0.324255, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=25, score=0.370989, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=24, score=0.463026, total=   0.2s
[CV] ..................... max_depth=25, score=0.425900, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=25, score=0.406429, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=25, score=0.434882, total=   0.2s
[CV] ..................... max_depth=22, score=0.455063, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] max_depth=25 ....................................................
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=23, score=0.461191, total=   0.2s
[CV] ..................... max_depth=21, score=0.416764, total=   0.2s
[CV] ..................... max_depth=23, score=0.457930, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.382536, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.385897, total=   0.2s
[CV] ..................... max_depth=24, score=0.314468, total=   0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.433215, total=   0.2s
[CV] ..................... max_depth=24, score=0.384215, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.354671, total=   0.2s
[CV] ..................... max_depth=25, score=0.404162, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.400831, total=   0.3s
[CV] max_depth=24 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=25, score=0.435018, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=24, score=0.395121, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=26, score=0.381649, total=   0.2s
[CV] ..................... max_depth=25, score=0.376715, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=25, score=0.449107, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=26, score=0.400979, total=   0.2s
[CV] ..................... max_depth=25, score=0.316733, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=24, score=0.415236, total=   0.2s
[CV] ..................... max_depth=24, score=0.425547, total=   0.3s
[CV] ..................... max_depth=25, score=0.450441, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.391765, total=   0.2s
[CV] ..................... max_depth=26, score=0.461962, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=26, score=0.322171, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=26, score=0.398628, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.392317, total=   0.3s
[CV] max_depth=26 ....................................................
[CV] max_depth=25 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.436643, total=   0.2s
[CV] ..................... max_depth=26, score=0.436549, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=26, score=0.339101, total=   0.1s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.389875, total=   0.2s
[CV] ..................... max_depth=26, score=0.415223, total=   0.2s
[CV] ..................... max_depth=26, score=0.425900, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.385860, total=   0.2s
[CV] ..................... max_depth=25, score=0.375879, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.395008, total=   0.2s
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=26, score=0.435921, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.362163, total=   0.1s
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
[CV] max_depth=25 ....................................................
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=24, score=0.392317, total=   0.2s
[CV] ..................... max_depth=25, score=0.398887, total=   0.2s
[CV] ..................... max_depth=25, score=0.471127, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=26, score=0.371683, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=26, score=0.370989, total=   0.2s
[CV] ..................... max_depth=25, score=0.406854, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=25, score=0.328956, total=   0.2s
[CV] ..................... max_depth=25, score=0.478195, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.450441, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.318298, total=   0.2s
[CV] ..................... max_depth=26, score=0.416777, total=   0.2s
[CV] max_depth=26 ....................................................
[CV] ..................... max_depth=25, score=0.462025, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=26, score=0.376997, total=   0.2s
[CV] ..................... max_depth=26, score=0.466439, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.379833, total=   0.2s
[CV] ..................... max_depth=26, score=0.395164, total=   0.3s
[CV] ..................... max_depth=26, score=0.380390, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=25, score=0.384896, total=   0.2s
[CV] ..................... max_depth=26, score=0.412254, total=   0.2s
[CV] ..................... max_depth=27, score=0.399266, total=   0.1s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.409555, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=25, score=0.357611, total=   0.2s
[CV] ..................... max_depth=26, score=0.400349, total=   0.2s
[CV] ..................... max_depth=26, score=0.361230, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=27, score=0.389406, total=   0.2s
[CV] ..................... max_depth=26, score=0.449107, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.462025, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.401630, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=27, score=0.430823, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=27, score=0.451704, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=27, score=0.415723, total=   0.2s
[CV] ..................... max_depth=27, score=0.471127, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=26, score=0.443243, total=   0.2s
[CV] ..................... max_depth=27, score=0.333617, total=   0.2s
[CV] max_depth=27 ....................................................
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] max_depth=27 ....................................................
[CV] ..................... max_depth=25, score=0.401655, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.443634, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.327643, total=   0.2s
[CV] ..................... max_depth=27, score=0.330449, total=   0.2s
[CV] ..................... max_depth=27, score=0.376626, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.393637, total=   0.2s
[CV] ..................... max_depth=27, score=0.410272, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.416777, total=   0.2s
[CV] ..................... max_depth=27, score=0.425547, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.454683, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.446786, total=   0.2s
[CV] ..................... max_depth=27, score=0.373285, total=   0.2s
[CV] ..................... max_depth=27, score=0.377681, total=   0.2s
[CV] ..................... max_depth=27, score=0.418831, total=   0.1s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.362135, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.403779, total=   0.2s
[CV] ..................... max_depth=27, score=0.372238, total=   0.2s
[CV] ..................... max_depth=27, score=0.437474, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.395164, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.402091, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=28, score=0.389406, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.405122, total=   0.2s
[CV] ..................... max_depth=28, score=0.431054, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=27, score=0.462025, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=28, score=0.397797, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=28, score=0.434783, total=   0.2s
[CV] ..................... max_depth=28, score=0.462191, total=   0.2s
[CV] ..................... max_depth=28, score=0.440925, total=   0.2s
[CV] ..................... max_depth=28, score=0.393637, total=   0.2s
[CV] max_depth=28 ....................................................
[CV] ..................... max_depth=28, score=0.406936, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.333617, total=   0.2s
[CV] ..................... max_depth=28, score=0.322171, total=   0.2s
[CV] ..................... max_depth=28, score=0.403259, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.330449, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.421665, total=   0.2s
[CV] ..................... max_depth=28, score=0.458826, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.461509, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.406649, total=   0.2s
[CV] ..................... max_depth=28, score=0.382246, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.373285, total=   0.2s
[CV] ..................... max_depth=28, score=0.369254, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.374598, total=   0.2s
[CV] ..................... max_depth=28, score=0.392898, total=   0.2s
[CV] ..................... max_depth=28, score=0.408831, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.407236, total=   0.2s
[CV] ..................... max_depth=28, score=0.437474, total=   0.2s
[CV] ..................... max_depth=28, score=0.357611, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.405122, total=   0.2s
[CV] ..................... max_depth=28, score=0.462025, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=28, score=0.390982, total=   0.2s
[CV] ..................... max_depth=29, score=0.409711, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=29, score=0.422248, total=   0.2s
[CV] ..................... max_depth=29, score=0.393429, total=   0.1s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=29, score=0.422796, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=29, score=0.464482, total=   0.2s
[CV] ..................... max_depth=29, score=0.400044, total=   0.2s
[CV] max_depth=29 ....................................................
[CV] ..................... max_depth=29, score=0.311228, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.340367, total=   0.2s
[CV] ..................... max_depth=29, score=0.300426, total=   0.2s
[CV] ..................... max_depth=29, score=0.436663, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.369254, total=   0.2s
[CV] ..................... max_depth=29, score=0.422724, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.417904, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................






    



[Parallel(n_jobs=-1)]: Done 748 tasks      | elapsed:    6.3s






    



[CV] ..................... max_depth=29, score=0.385636, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.406649, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.477057, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.390698, total=   0.3s
[CV] ..................... max_depth=29, score=0.374943, total=   0.2s
[CV] ..................... max_depth=29, score=0.464416, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.392898, total=   0.2s
[CV] ..................... max_depth=29, score=0.381810, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.374598, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.371409, total=   0.2s
[CV] ..................... max_depth=29, score=0.397727, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.434762, total=   0.2s
[CV] ..................... max_depth=29, score=0.462025, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.376330, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.405122, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.464482, total=   0.2s
[CV] ..................... max_depth=29, score=0.390982, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=29, score=0.441213, total=   0.3s
[CV] ..................... max_depth=30, score=0.406936, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.426130, total=   0.2s
[CV] ..................... max_depth=30, score=0.431022, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.392901, total=   0.2s
[CV] ..................... max_depth=30, score=0.393221, total=   0.2s
[CV] max_depth=30 ....................................................
[CV] max_depth=30 ....................................................
[CV] ..................... max_depth=30, score=0.313417, total=   0.2s
[CV] ..................... max_depth=30, score=0.324255, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.340367, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.377710, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.403259, total=   0.2s
[CV] ..................... max_depth=30, score=0.421313, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.464416, total=   0.1s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.375343, total=   0.2s
[CV] ..................... max_depth=30, score=0.416557, total=   0.2s
[CV] ..................... max_depth=30, score=0.477057, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.370521, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.397727, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.379174, total=   0.2s
[CV] ..................... max_depth=30, score=0.394787, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.371409, total=   0.2s
[CV] ..................... max_depth=31, score=0.389849, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.345733, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.397953, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.447029, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.413271, total=   0.2s
[CV] ..................... max_depth=31, score=0.409711, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=31, score=0.434762, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=30, score=0.448622, total=   0.3s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=31, score=0.429881, total=   0.2s
[CV] ..................... max_depth=31, score=0.439483, total=   0.2s
[CV] max_depth=31 ....................................................
[CV] max_depth=31 ....................................................
[CV] ..................... max_depth=31, score=0.390453, total=   0.2s
[CV] ..................... max_depth=31, score=0.380744, total=   0.2s
[CV] ..................... max_depth=31, score=0.464711, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.330707, total=   0.2s
[CV] ..................... max_depth=31, score=0.337624, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.366651, total=   0.2s
[CV] ..................... max_depth=31, score=0.406559, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.323830, total=   0.3s
[CV] ..................... max_depth=31, score=0.421313, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.456461, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.414575, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.376529, total=   0.2s
[CV] ..................... max_depth=31, score=0.401206, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.377710, total=   0.3s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.452430, total=   0.2s
[CV] ..................... max_depth=31, score=0.385755, total=   0.2s
[CV] ..................... max_depth=31, score=0.360552, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.395164, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.452408, total=   0.3s
[CV] ..................... max_depth=32, score=0.390453, total=   0.2s
[CV] ..................... max_depth=31, score=0.414047, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.345733, total=   0.3s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=32, score=0.303830, total=   0.2s
[CV] ..................... max_depth=32, score=0.384530, total=   0.2s
[CV] ..................... max_depth=32, score=0.432445, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.394332, total=   0.3s
[CV] ..................... max_depth=32, score=0.442068, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=31, score=0.448622, total=   0.3s
[CV] ..................... max_depth=32, score=0.427052, total=   0.2s
[CV] ..................... max_depth=32, score=0.325454, total=   0.2s
[CV] max_depth=32 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=32, score=0.444051, total=   0.2s
[CV] ..................... max_depth=32, score=0.337413, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=32 ....................................................
[CV] ..................... max_depth=32, score=0.476398, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.399043, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.405116, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.429076, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.361391, total=   0.2s
[CV] ..................... max_depth=32, score=0.370772, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.418098, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.461509, total=   0.2s
[CV] ..................... max_depth=32, score=0.394332, total=   0.2s
[CV] ..................... max_depth=32, score=0.370835, total=   0.2s
[CV] ..................... max_depth=32, score=0.447216, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.370521, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.399042, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.428334, total=   0.2s
[CV] ..................... max_depth=33, score=0.384530, total=   0.2s
[CV] ..................... max_depth=32, score=0.395164, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=32, score=0.363266, total=   0.2s
[CV] ..................... max_depth=32, score=0.414047, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=33, score=0.427346, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=33, score=0.445093, total=   0.2s
[CV] ..................... max_depth=32, score=0.397032, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=33, score=0.319149, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=33, score=0.434783, total=   0.2s
[CV] ..................... max_depth=33, score=0.464711, total=   0.2s
[CV] ..................... max_depth=32, score=0.468354, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=33 ....................................................
[CV] ..................... max_depth=33, score=0.313417, total=   0.2s
[CV] max_depth=33 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.390942, total=   0.2s
[CV] ..................... max_depth=33, score=0.391765, total=   0.2s
[CV] ..................... max_depth=33, score=0.413642, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.335514, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.405116, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.376409, total=   0.2s
[CV] ..................... max_depth=33, score=0.429076, total=   0.2s
[CV] ..................... max_depth=33, score=0.369854, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.452591, total=   0.2s
[CV] ..................... max_depth=33, score=0.394332, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.388364, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.363266, total=   0.2s
[CV] ..................... max_depth=33, score=0.404923, total=   0.2s
[CV] ..................... max_depth=33, score=0.367412, total=   0.2s
[CV] ..................... max_depth=33, score=0.373310, total=   0.2s
[CV] ..................... max_depth=33, score=0.397032, total=   0.2s
[CV] ..................... max_depth=33, score=0.462268, total=   0.2s
[CV] ..................... max_depth=34, score=0.386303, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.427346, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.468354, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=33, score=0.414575, total=   0.3s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.413642, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.382864, total=   0.2s
[CV] ..................... max_depth=33, score=0.454092, total=   0.2s
[CV] ..................... max_depth=33, score=0.426465, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.440893, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.319149, total=   0.2s
[CV] ..................... max_depth=34, score=0.318232, total=   0.2s
[CV] max_depth=34 ....................................................
[CV] max_depth=34 ....................................................
[CV] ..................... max_depth=34, score=0.445093, total=   0.2s
[CV] ..................... max_depth=34, score=0.461503, total=   0.2s
[CV] ..................... max_depth=34, score=0.392389, total=   0.2s
[CV] ..................... max_depth=34, score=0.420607, total=   0.2s
[CV] ..................... max_depth=34, score=0.434960, total=   0.1s
[CV] ..................... max_depth=34, score=0.412748, total=   0.2s
[CV] ..................... max_depth=34, score=0.337413, total=   0.2s
[CV] ..................... max_depth=34, score=0.349612, total=   0.1s
[CV] ..................... max_depth=34, score=0.374657, total=   0.2s
[CV] ..................... max_depth=34, score=0.474782, total=   0.2s
[CV] ..................... max_depth=34, score=0.400705, total=   0.2s
[CV] ..................... max_depth=34, score=0.368387, total=   0.2s
[CV] ..................... max_depth=34, score=0.401582, total=   0.2s
[CV] ..................... max_depth=34, score=0.381892, total=   0.2s
[CV] ..................... max_depth=34, score=0.363266, total=   0.1s
[CV] ..................... max_depth=34, score=0.396675, total=   0.2s
[CV] ..................... max_depth=34, score=0.433735, total=   0.2s
[CV] ..................... max_depth=34, score=0.397032, total=   0.2s
[CV] ..................... max_depth=34, score=0.402794, total=   0.2s
[CV] ..................... max_depth=34, score=0.404923, total=   0.2s
[CV] ..................... max_depth=34, score=0.440060, total=   0.2s






    



[Parallel(n_jobs=-1)]: Done 924 out of 924 | elapsed:    8.0s finished



In [29]:

    
pd.DataFrame(clf.cv_results_).sort_values(by="rank_test_score").head()









    Out[29]:






  
    
      
      mean_fit_time
      mean_score_time
      mean_test_score
      mean_train_score
      param_max_depth
      params
      rank_test_score
      split0_test_score
      split0_train_score
      split10_test_score
      ...
      split7_test_score
      split7_train_score
      split8_test_score
      split8_train_score
      split9_test_score
      split9_train_score
      std_fit_time
      std_score_time
      std_test_score
      std_train_score
    
  
  
    
      3
      0.087601
      0.002639
      0.454242
      0.600811
      5
      {'max_depth': 5}
      1
      0.382979
      0.628079
      0.407904
      ...
      0.495103
      0.594984
      0.438553
      0.621851
      0.394178
      0.635939
      0.016670
      0.000414
      0.046857
      0.016661
    
    
      5
      0.108709
      0.002626
      0.449962
      0.683652
      7
      {'max_depth': 7}
      2
      0.419548
      0.707580
      0.376853
      ...
      0.466972
      0.694516
      0.405906
      0.700963
      0.399650
      0.725266
      0.010878
      0.000403
      0.044412
      0.014936
    
    
      4
      0.098397
      0.002562
      0.446364
      0.641523
      6
      {'max_depth': 6}
      3
      0.414229
      0.660637
      0.401553
      ...
      0.451969
      0.644529
      0.421501
      0.656767
      0.346465
      0.681555
      0.013679
      0.000392
      0.051968
      0.014949
    
    
      2
      0.075952
      0.003243
      0.445336
      0.558320
      4
      {'max_depth': 4}
      4
      0.393617
      0.581826
      0.346860
      ...
      0.465930
      0.549501
      0.372427
      0.565367
      0.394397
      0.601143
      0.018185
      0.003917
      0.054661
      0.017602
    
    
      7
      0.154772
      0.002851
      0.442784
      0.770599
      9
      {'max_depth': 9}
      5
      0.395390
      0.791301
      0.395907
      ...
      0.447385
      0.791398
      0.401539
      0.796340
      0.343839
      0.804034
      0.027358
      0.000541
      0.040286
      0.015717
    
  

5 rows × 67 columns



In [30]:

    
clf_dt = tree.DecisionTreeClassifier(max_depth=5)
clf_dt.fit(X_train, y_train)
predicted_labels = clf_dt.predict(X_test)

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS    55    58    21                 1                     135
     CSiS    14   237    87     5                                 343
     FSiS     2    96   186     4           1                     289
     SiSh     1           7    48          49                 1   106
       MS           2     1    12          90     1     2     2   110
       WS                 2    17         183     1    22     4   229
        D                 1    31          36     7     5     2    82
       PS           1     1    26          70     2   114    14   228
       BS                       4          14          23    50    91

Precision  0.76  0.60  0.61  0.33  0.00  0.41  0.64  0.69  0.68  0.55
   Recall  0.41  0.69  0.64  0.45  0.00  0.80  0.09  0.50  0.55  0.55
       F1  0.53  0.64  0.63  0.38  0.00  0.54  0.15  0.58  0.61  0.52

Random Forest classifier



In [31]:

    
from sklearn.ensemble import RandomForestClassifier

param_grid = {"max_depth": [3, None],
              "max_features": [1, 3, 7],
#               "min_samples_split": [1, 3, 10],
              "min_samples_leaf": [1, 3, 10],
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}

clf_rf = RandomForestClassifier(n_estimators=200)
clf = LPWO_CV(clf_rf, param_grid)









    



Fitting 28 folds for each of 72 candidates, totalling 2016 fits
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420578, total=   2.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.492778, total=   2.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.494554, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.397172, total=   2.1s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.351062, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.424677, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.455316, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.370120, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.461405, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.414191, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.476974, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.493227, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.364583, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.461251, total=   2.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.377872, total=   2.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.417838, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.483797, total=   2.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.425557, total=   2.4s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.453551, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.286521, total=   2.5s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.493596, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.517241, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.391009, total=   2.6s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.491468, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.464169, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420267, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.352615, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.483430, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.416216, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.459887, total=   2.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.393637, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.442975, total=   2.1s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.420998, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.348435, total=   2.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.374552, total=   2.1s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.453025, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.487420, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.495226, total=   2.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.357392, total=   2.6s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.481617, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.435051, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.373191, total=   2.5s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.412486, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.408042, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.476495, total=   2.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.265702, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.459613, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.376275, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.420035, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.448187, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.431124, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.422890, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.473317, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.339761, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.426020, total=   2.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.451344, total=   2.1s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.499633, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.503592, total=   2.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.415041, total=   2.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.391142, total=   2.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.406147, total=   2.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.369909, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.425168, total=   2.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.462489, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.508101, total=   2.6s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.355877, total=   2.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.476856, total=   2.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.374468, total=   2.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.491766, total=   2.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.355452, total=   2.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.260762, total=   2.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.477436, total=   2.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.484627, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.423469, total=   2.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.471158, total=   2.5s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.452263, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.453496, total=   2.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.428163, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.481972, total=   2.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.415860, total=   2.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.501870, total=   2.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.443112, total=   2.5s






    



[Parallel(n_jobs=-1)]: Done  80 tasks      | elapsed:   12.2s






    



[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.383831, total=   2.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.367482, total=   2.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.358599, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.450529, total=   3.8s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.512167, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.427746, total=   3.9s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.497938, total=   3.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.478433, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.389478, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.487638, total=   4.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.384767, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.415488, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.347660, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.293931, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.454425, total=   3.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.431700, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.488590, total=   3.8s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.509149, total=   4.0s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.432962, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.450226, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.473858, total=   3.8s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.423729, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.542662, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.472866, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.392898, total=   4.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.488160, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.418231, total=   3.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.363032, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.454585, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.516570, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.415600, total=   4.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.490330, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.425202, total=   4.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.426284, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.480752, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.449589, total=   4.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.352340, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.482392, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.371197, total=   3.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.391142, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.433435, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.436262, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.406204, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.505261, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.294989, total=   4.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.447160, total=   4.0s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.457321, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.538491, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.487220, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.395164, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.463093, total=   4.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.424802, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.477505, total=   3.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.414160, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.468090, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.486498, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.357934, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.431676, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.482252, total=   3.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.427401, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.513094, total=   4.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.418316, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.486939, total=   3.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.481350, total=   3.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.439953, total=   4.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.350213, total=   4.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.393429, total=   4.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.437294, total=   3.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.405993, total=   3.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.380608, total=   4.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.423461, total=   3.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.296754, total=   4.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.502516, total=   4.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.444077, total=   3.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.449366, total=   3.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.530906, total=   4.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.486764, total=   3.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.422656, total=   3.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.466828, total=   3.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.397431, total=   4.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.421398, total=   3.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.470083, total=   4.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.484420, total=   4.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.428146, total=   4.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.365153, total=   4.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.460684, total=   4.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.409480, total=   7.3s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.489687, total=   7.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.337323, total=   7.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.391765, total=   6.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.430600, total=   7.5s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.452174, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.449052, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.503437, total=   7.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.399869, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.390800, total=   6.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.316596, total=   8.1s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.428630, total=   6.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.522873, total=   7.2s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.477450, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.556803, total=   7.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.271701, total=   8.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.445496, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.490334, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.479690, total=   7.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.512704, total=   8.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.406565, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.471243, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.394787, total=   8.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420856, total=   8.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.492267, total=   7.1s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.415517, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.337323, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.409249, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.430845, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.489919, total=   7.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.449052, total=   6.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.452174, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.498854, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.447419, total=   8.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.400525, total=   6.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.451229, total=   8.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.391765, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.320426, total=   8.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.427599, total=   6.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.392066, total=   7.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.270289, total=   8.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.483304, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.556583, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.517841, total=   7.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.455386, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.486079, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.513083, total=   8.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.395542, total=   8.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.406994, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.490334, total=   7.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.474258, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.406017, total=   7.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.496842, total=   7.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.337544, total=   7.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.392370, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.489687, total=   7.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.431334, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.420440, total=   8.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.447031, total=   8.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.453114, total=   7.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.449367, total=   8.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.448844, total=   6.9s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.497709, total=   7.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.391973, total=   6.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.324255, total=   8.2s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.399650, total=   7.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.391855, total=   6.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.426361, total=   7.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.478317, total=   7.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.271348, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.517612, total=   7.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.513462, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 






    



[Parallel(n_jobs=-1)]: Done 240 tasks      | elapsed:   54.8s






    



[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.556363, total=   7.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.427305, total=   5.3s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.447431, total=   7.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.493873, total=   5.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.479461, total=   7.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.552491, total=   5.7s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.406136, total=   7.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.490993, total=   7.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.493268, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.500118, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.548808, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.392898, total=   8.1s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.474258, total=   7.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.416422, total=   7.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.415679, total=   5.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.499238, total=   7.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.498020, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.399149, total=   6.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.421271, total=   8.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.447419, total=   8.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.409280, total=   5.8s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.449367, total=   8.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.425900, total=   6.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.392066, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.455652, total=   5.4s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.472029, total=   5.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.523778, total=   5.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.518527, total=   5.7s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.538379, total=   5.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.458466, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.489895, total=   5.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.476936, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.438387, total=   5.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.473875, total=   5.4s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.621919, total=   6.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.493738, total=   5.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.492255, total=   6.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.560139, total=   5.2s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.527336, total=   5.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.511908, total=   5.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.528043, total=   6.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.578928, total=   6.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.562282, total=   6.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.523384, total=   5.3s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.484700, total=   5.5s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.406809, total=   6.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.556599, total=   5.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.505522, total=   5.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.418590, total=   4.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.396805, total=   5.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.398396, total=   4.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.466997, total=   5.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.459237, total=   5.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.531793, total=   5.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.419548, total=   6.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.534566, total=   5.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.543109, total=   5.3s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.490554, total=   5.1s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.477365, total=   5.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.626090, total=   5.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.456184, total=   5.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.430629, total=   4.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.473648, total=   5.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.495826, total=   5.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.506611, total=   5.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.530951, total=   6.0s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.577057, total=   4.7s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.526900, total=   5.2s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.494002, total=   4.6s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.558401, total=   5.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.502428, total=   5.1s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.583395, total=   5.9s
[CV] max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.529495, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.574931, total=   4.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.440632, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.501146, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.403404, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.401620, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.388057, total=   4.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.470710, total=   4.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.525160, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.516292, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.478777, total=   4.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.411080, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.542464, total=   4.7s
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.462056, total=   5.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.502896, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.618506, total=   5.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.500189, total=   5.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.496289, total=   4.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.490993, total=   4.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.554999, total=   4.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.523058, total=   5.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.487446, total=   4.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.551028, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.580045, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.431959, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.543685, total=  11.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.473295, total=  11.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.477601, total=  11.8s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.420462, total=  10.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.498020, total=  10.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.527039, total=  11.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.488132, total=  11.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.391011, total=  10.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.458746, total=  10.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.383454, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.515096, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.441891, total=  11.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.522038, total=  11.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.434961, total=  11.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.520916, total=  11.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.371915, total=  13.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.466608, total=  11.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.469642, total=  11.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.425547, total=  12.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.605612, total=  13.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.475649, total=  11.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.495278, total=  13.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.506440, total=  13.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.454196, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.435062, total=  10.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.478613, total=  10.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.548552, total=  11.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.515356, total=  11.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.418174, total=   9.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.497395, total=  10.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.502468, total=  10.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.533226, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.482252, total=  11.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.387831, total=  10.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.462459, total=  10.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.392910, total=  10.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.538223, total=  13.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.572599, total=  13.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.443842, total=  10.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.513495, total=  10.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.393191, total=  13.1s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.528181, total=  10.4s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.523973, total=  10.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.426958, total=  12.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.438384, total=  10.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.615472, total=  12.3s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.488478, total=  12.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.466638, total=  10.2s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.466828, total=  11.0s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.549015, total=   9.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.435727, total=   9.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.485087, total=   9.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.475649, total=  10.6s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.498854, total=   8.9s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.456232, total=  10.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.491554, total=  10.0s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.427948, total=   8.8s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.519277, total=  10.5s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.557745, total=   9.7s
[CV] max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.497767, total=  10.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.394178, total=   9.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.501870, total=  12.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.391433, total=   9.4s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.404255, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.531238, total=  12.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.460396, total=   9.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.573716, total=  12.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.447745, total=   9.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.510522, total=   9.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.417431, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.607888, total=  11.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.527301, total=   9.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.539669, total=   9.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.467925, total=   9.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.443633, total=   9.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.473638, total=   9.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.475186, total=   9.6s
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.467315, total=   9.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.528425, total=   9.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.489233, total=  11.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.513087, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.581534, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.517656, total=  11.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.481142, total=  21.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.413564, total=  23.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.405490, total=  21.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.499421, total=  23.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.353688, total=  21.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.453873, total=  23.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.505041, total=  23.5s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456548, total=  23.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.462045, total=  24.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.429781, total=  27.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.377084, total=  23.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.438531, total=  22.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.369787, total=  28.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.423244, total=  22.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.476027, total=  22.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.464318, total=  23.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.473800, total=  23.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.427659, total=  23.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.452548, total=  23.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.454409, total=  23.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.448748, total=  23.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.582101, total=  27.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.475255, total=  27.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.501039, total=  28.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.414894, total=  21.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.514484, total=  21.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.437005, total=  23.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.491396, total=  23.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.463158, total=  22.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.458960, total=  22.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.480308, total=  20.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.544304, total=  27.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.405906, total=  20.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.521537, total=  28.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.525894, total=  22.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.469800, total=  22.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.385642, total=  21.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.377928, total=  21.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.388085, total=  27.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.440800, total=  20.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.428014, total=  21.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.461574, total=  22.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.487637, total=  21.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.486790, total=  22.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 






    



[Parallel(n_jobs=-1)]: Done 464 tasks      | elapsed:  2.8min






    



[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.430840, total=  26.2s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.429484, total=  22.0s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.586272, total=  25.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.483189, total=  25.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.455844, total=  22.8s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.452263, total=  22.3s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.454545, total=  22.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.429743, total=  19.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.521205, total=  19.9s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.443791, total=  23.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.477599, total=  18.5s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.457772, total=  20.1s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.466590, total=  20.7s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.495753, total=  22.4s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.530477, total=  19.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.498546, total=  26.6s
[CV] max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.410688, total=  18.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.528522, total=  26.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.477086, total=  20.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.395491, total=  18.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.549516, total=  26.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.380671, total=  19.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.451320, total=  18.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.392340, total=  24.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.432784, total=  20.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.348183, total=   5.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.469061, total=   5.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.401156, total=   5.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.437897, total=  23.0s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.470952, total=  19.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.492534, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.455928, total=   5.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420917, total=   6.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.444546, total=   6.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.364681, total=   6.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.342088, total=   5.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.376378, total=   5.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.417492, total=   5.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.283345, total=   6.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.374130, total=   5.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.447095, total=   5.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.510428, total=  18.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.609405, total=  23.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.477127, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.485249, total=  20.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.443633, total=  20.2s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.480542, total=   5.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.440114, total=   6.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.472159, total=   6.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.455442, total=   6.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.461274, total=  20.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.417838, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.454525, total=  21.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.368342, total=   6.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420719, total=   6.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.449220, total=  20.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.435743, total=   6.7s
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.448516, total=  21.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.505193, total=   7.1s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.415353, total=   7.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.503158, total=  20.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.480544, total=  24.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.361661, total=   6.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.438031, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.404319, total=   6.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.458169, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.348183, total=   6.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.489596, total=   6.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.406936, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.515164, total=  24.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.416921, total=   6.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.570365, total=  24.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.373191, total=   7.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.444546, total=   6.8s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.335084, total=   6.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.376378, total=   6.9s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.452178, total=   7.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.521537, total=  26.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.447095, total=   6.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.409035, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.370964, total=   6.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.421400, total=   6.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.268878, total=   7.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.461116, total=   6.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.437619, total=   7.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.472159, total=   6.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.471512, total=   6.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.417838, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.379675, total=   7.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.435743, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.423006, total=   6.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.418684, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.505193, total=   7.1s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.451536, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.358556, total=   7.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.403202, total=   6.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.348626, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.414104, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.480185, total=   6.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.496940, total=   6.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.420917, total=   6.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.447984, total=   5.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.389478, total=   5.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.463013, total=   6.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.342088, total=   5.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.370638, total=   7.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.371597, total=   5.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.405116, total=   5.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.270642, total=   6.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.477127, total=   6.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.439072, total=   6.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.439515, total=   7.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.428666, total=   6.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.474307, total=   6.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.466454, total=   6.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.401362, total=   6.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.435743, total=   6.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.378542, total=   7.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.421850, total=   6.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.506024, total=   6.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.419295, total=   6.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.438031, total=   6.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.358556, total=   7.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.402829, total=   7.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.360372, total=  14.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.484211, total=  15.1s
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.450985, total=  15.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.417341, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.480517, total=  14.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.427967, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.440192, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.283870, total=  14.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.390102, total=  14.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.410846, total=  14.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.433787, total=  15.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.409367, total=  14.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.348085, total=  17.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.469122, total=  15.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.293225, total=  17.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.422060, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.436592, total=  15.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.473528, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.457381, total=  14.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.406351, total=  15.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.483883, total=  17.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.438080, total=  15.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.376275, total=  17.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.508517, total=  17.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.410314, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.392943, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.349734, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.495716, total=  15.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.450521, total=  15.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.437225, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.427967, total=  15.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.379375, total=  17.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.403758, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.393637, total=  14.6s
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.339930, total=  17.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.337273, total=  14.3s
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.478433, total=  14.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.346383, total=  17.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.429868, total=  14.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.418232, total=  15.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.420859, total=  15.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.468893, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.289697, total=  16.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.425930, total=  14.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.433069, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.473528, total=  14.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.487675, total=  17.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.370608, total=  17.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.448814, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.409998, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.438080, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.391767, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.352172, total=  14.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.434328, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.482742, total=  14.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.463036, total=  15.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.431908, total=  15.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.520980, total=  17.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.471557, total=  14.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.440192, total=  15.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.404734, total=  17.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.390934, total=  14.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.405063, total=  17.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.440188, total=  15.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.274239, total=  15.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.346383, total=  17.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.408103, total=  14.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.433787, total=  14.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.421292, total=  15.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.286168, total=  17.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.469122, total=  15.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.472886, total=  17.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.433069, total=  15.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.422060, total=  15.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.473528, total=  15.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.458480, total=  15.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.408925, total=  15.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.412350, total=  15.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.447588, total=  15.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.427358, total=  14.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.370608, total=  18.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.502285, total=  17.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.365541, total=  17.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.406925, total=  17.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.395168, total=  31.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.405386, total=  32.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.440092, total=  31.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.444264, total=  33.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.390518, total=  31.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.304224, total=  30.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.408092, total=  34.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.455699, total=  33.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.419798, total=  33.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.342128, total=  38.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.406837, total=  31.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.291108, total=  37.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.400578, total=  31.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.420425, total=  32.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.471866, total=  33.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.520916, total=  33.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.427435, total=  32.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.433592, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.432118, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.389187, total=  32.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.443414, total=  33.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.455442, total=  39.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.384209, total=  38.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.397175, total=  38.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.399231, total=  32.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.395168, total=  31.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.471139, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.405386, total=  32.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.444264, total=  33.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.405780, total=  34.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.433450, total=  38.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=1, score=0.451229, total=  39.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.445828, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.418882, total=  32.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.344255, total=  38.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.390518, total=  31.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.441134, total=  32.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.319982, total=  30.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.400990, total=  31.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.387424, total=  32.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.424761, total=  33.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.470952, total=  33.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.292167, total=  37.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.516292, total=  33.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.428725, total=  32.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.431766, total=  32.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.455442, total=  38.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.384964, total=  38.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.430360, total=  33.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.389616, total=  33.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.399231, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.416976, total=  34.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.380541, total=  32.1s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.433450, total=  38.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.396759, total=  39.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.471139, total=  34.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.445423, total=  33.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.405386, total=  33.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.404162, total=  34.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.446769, total=  33.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.426964, total=  31.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=3, score=0.440432, total=  38.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.418882, total=  34.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.342128, total=  39.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.316699, total=  31.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.386567, total=  33.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.400784, total=  32.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.395020, total=  33.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.417606, total=  33.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.471409, total=  33.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.291814, total=  38.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.427748, total=  16.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.551564, total=  17.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.492486, total=  17.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.516292, total=  34.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.498164, total=  17.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.519859, total=  17.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.427220, total=  35.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.491561, total=  15.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.557745, total=  17.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 






    



[Parallel(n_jobs=-1)]: Done 752 tasks      | elapsed:  6.8min






    



[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.455821, total=  40.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.426746, total=  34.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.398723, total=  21.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.417758, total=  16.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.403371, total=  15.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.434095, total=  33.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.403529, total=  33.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.389187, total=  34.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.443414, total=  34.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.384586, total=  39.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.471139, total=  33.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.412844, total=  20.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.395231, total=  17.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456064, total=  17.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456418, total=  16.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.440432, total=  39.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.433450, total=  40.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=3, min_samples_leaf=10, score=0.399668, total=  41.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.514181, total=  17.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.520476, total=  17.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.535369, total=  17.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.462118, total=  17.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.475615, total=  17.5s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.612438, total=  20.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.475005, total=  17.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.504174, total=  16.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.484506, total=  17.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.491878, total=  20.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.533029, total=  21.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.432181, total=  15.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.550174, total=  16.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.527772, total=  17.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.487638, total=  16.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.501503, total=  17.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.564998, total=  21.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.569620, total=  20.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.497187, total=  15.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.412352, total=  15.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.520564, total=  16.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.557287, total=  16.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.396586, total=  15.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.398723, total=  19.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.463284, total=  15.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.389956, total=  16.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.417431, total=  18.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.525160, total=  16.0s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.454033, total=  16.6s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.466682, total=  16.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.520255, total=  17.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.539239, total=  16.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.619264, total=  20.4s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.487039, total=  16.9s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.480154, total=  17.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.491878, total=  20.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.503015, total=  16.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.474553, total=  17.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.434176, total=  14.2s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.536049, total=  16.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.530536, total=  19.7s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.495716, total=  15.1s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.547148, total=  19.0s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.570800, total=  15.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.495260, total=  15.8s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.533020, total=  16.3s
[CV] max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.577066, total=  20.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.438345, total=  13.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.498229, total=  14.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.411064, total=  18.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.400306, total=  14.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.561182, total=  15.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.380038, total=  14.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.467203, total=  14.3s
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.448395, total=  14.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.407904, total=  17.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.524016, total=  15.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.528402, total=  16.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.473528, total=  15.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.540314, total=  15.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.497364, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.495387, total=  15.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.615472, total=  19.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.489708, total=  15.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.501855, total=  15.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.530536, total=  17.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.493767, total=  18.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.539970, total=  15.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.540163, total=  19.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.575205, total=  19.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.434619, total=  37.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.542990, total=  41.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.410896, total=  38.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.529560, total=  40.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.462197, total=  45.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.493772, total=  44.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.483966, total=  44.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.383016, total=  41.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.484268, total=  43.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456889, total=  39.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.437988, total=  41.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.389956, total=  42.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.509835, total=  42.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.447969, total=  41.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.517523, total=  41.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.382553, total=  55.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.500000, total=  45.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456063, total=  45.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.605612, total=  48.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.424841, total=  52.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456769, total=  45.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.482811, total=  49.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.477505, total=  46.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.528459, total=  53.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.461208, total=  40.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.498366, total=  41.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.543453, total=  39.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.441268, total=  42.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.496592, total=  40.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.495961, total=  43.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.477225, total=  44.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.412144, total=  37.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.521925, total=  49.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.573343, total=  49.8s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.537351, total=  40.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.391552, total=  39.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.490936, total=  42.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.386157, total=  40.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.461840, total=  41.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.432957, total=  44.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.386383, total=  53.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.508692, total=  39.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.452082, total=  44.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.506825, total=  43.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.520103, total=  41.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.447056, total=  42.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.605233, total=  53.4s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.463533, total=  42.1s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.477900, total=  51.2s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.463205, total=  39.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.441046, total=  35.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.467994, total=  39.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.499878, total=  36.3s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.477919, total=  37.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.546698, total=  38.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.475649, total=  43.5s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.505772, total=  40.9s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.493436, total=  33.6s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.529705, total=  47.7s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.401401, total=  34.0s
[CV] max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.420878, total=  37.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.501998, total=  42.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.550412, total=  41.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.465347, total=  35.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.396596, total=  46.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.383203, total=  38.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.569248, total=  52.9s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.525805, total=  53.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.443192, total=  40.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.410727, total=  44.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.514867, total=  39.3s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.509247, total=  39.2s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.530424, total=  37.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.452305, total=  36.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.606371, total=  43.6s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.479082, total=  37.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.469025, total=  38.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.480660, total=  37.8s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.475881, total=  41.4s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.483189, total=  44.0s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.516445, total=  38.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.522227, total=  45.5s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.569248, total=  44.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.523477, total=  45.7s
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.426418, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.487882, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.478850, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.405906, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.350186, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.520046, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.454104, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.493355, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.470975, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.452145, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.378983, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.431483, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.456313, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.363830, total= 2.0min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.487422, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.418490, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.468076, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.445299, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.428571, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.438318, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.448980, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.562002, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.476011, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.528874, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.424202, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.462791, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.518192, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.461965, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.490330, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.480308, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.486169, total= 1.6min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.409441, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.467450, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.496563, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.499030, total= 1.9min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.384767, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=1, score=0.547655, total= 1.9min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.453383, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.385102, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.438638, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.365532, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.457457, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.473360, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.422724, total= 1.7min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.434733, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.498387, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.563140, total= 1.8min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.476766, total= 1.7min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.454194, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.450132, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.432181, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.463017, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.452458, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.524913, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.459884, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.478016, total= 1.2min
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.510404, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.486604, total= 1.5min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.410480, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.467920, total= 1.4min
[CV] max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.515582, total= 1.4min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.399431, total= 1.2min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.524304, total= 1.7min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.362589, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.497567, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.423815, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.388901, total= 1.2min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.503060, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.368085, total=   3.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.552494, total= 1.8min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=3, score=0.504075, total= 1.8min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.430787, total=   3.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.460809, total= 1.2min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.465170, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.470306, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.389894, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.350843, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.452515, total= 1.3min
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.441023, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.276641, total=   3.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.376240, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.411922, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.376170, total= 1.6min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.496340, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.486917, total=   3.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.461471, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.471018, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.485272, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.443896, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.392520, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.417838, total=   3.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.506855, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.431818, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.425469, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.371362, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.418490, total= 1.5min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.416977, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.355053, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.501506, total=   2.8s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.462644, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.500612, total=   2.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.423121, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.370213, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.423972, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.475932, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.472044, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.386775, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.342088, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.395215, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.386579, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.257586, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.451214, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.467063, total= 1.3min
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.499314, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.455746, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.491722, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.481059, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.475161, total=   3.5s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.439176, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.423111, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.377786, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.425093, total=   3.0s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.500623, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.415969, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.358168, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.414371, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.421272, total=   2.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.361924, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.453278, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.487370, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.494982, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.379149, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.451650, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.429377, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.470306, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.397796, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.342088, total=   2.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.418936, total=   2.7s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.378139, total=   2.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.289697, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.449046, total=   3.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.485590, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.475161, total=   3.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.440555, total=   2.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.483552, total=   2.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.471018, total=   2.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.374386, total=   3.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.417838, total=   2.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.439391, total=   3.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.430672, total=   2.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.500623, total=   3.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.430659, total=   3.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.362049, total=   3.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.460030, total=   2.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.413999, total=   2.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.363475, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.534647, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.424740, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.488862, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.456639, total=   5.7s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.481350, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.502979, total=   5.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.394261, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.348085, total=   6.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.361348, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.296401, total=   6.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.411479, total=   5.7s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.435231, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.437337, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.511208, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.440995, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.459471, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.488590, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.474736, total=   5.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.510808, total=   6.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.395164, total=   6.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.422442, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.472635, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.392219, total=   5.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.483590, total=   7.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 






    



[Parallel(n_jobs=-1)]: Done 1104 tasks      | elapsed: 15.7min






    



[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.419092, total=   6.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.414743, total=   6.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.365913, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.459813, total=   6.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.532793, total=   5.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.473195, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.426358, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.568828, total= 1.6min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.348085, total=   6.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.448414, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.504354, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.481767, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.396756, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.400717, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.360035, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.440182, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.509835, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.427580, total=   5.9s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.295695, total=   6.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.430625, total= 1.3min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.440995, total=   5.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.503303, total= 1.4min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.501183, total= 1.4min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.525218, total=   6.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.437540, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.477179, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.468146, total=   5.7s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.421154, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.398564, total=   6.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.475186, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.482343, total=   6.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.386790, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.444348, total=   5.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.368794, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.408414, total=   6.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.540440, total=   5.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.420256, total=   6.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.425896, total=   5.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.486169, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.448374, total= 1.4min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.357447, total=   6.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.439483, total=   5.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.468446, total= 1.3min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.510999, total=   5.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.451067, total= 1.3min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.473849, total=   5.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.396964, total=   5.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.472002, total= 1.4min
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.328518, total=   5.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.408525, total=   5.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.438325, total=   5.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.292167, total=   6.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.511208, total=   5.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.429748, total=   5.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.445619, total=   5.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.524080, total=   6.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.468286, total=   5.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.484254, total=   5.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.392142, total=   6.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.468146, total=   5.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.419009, total=   5.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.493560, total=   6.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.410767, total=   5.6s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.475417, total=   5.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.454803, total=   5.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.418838, total=   6.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.419868, total=   6.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.491396, total= 1.4min
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.473366, total= 1.6min
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.337323, total=  10.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.488297, total=  11.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.390520, total=  11.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.425949, total=  11.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.450733, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.450294, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.391765, total=  10.7s
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.452386, total=  10.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.529290, total= 1.6min
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.316043, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.311915, total=  13.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.371386, total=  11.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.417904, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.474198, total=  11.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.270995, total=  13.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.520586, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.556583, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.408729, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.482656, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.505119, total=  13.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.474956, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.355497, total=  12.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.409140, total=  10.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.562547, total= 1.7min
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=True, max_depth=None, min_samples_leaf=10, score=0.508343, total= 1.7min
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.492115, total=  11.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.406017, total=  11.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.337323, total=  11.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.482466, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.488297, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.415455, total=  13.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.446643, total=  12.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.390520, total=  11.5s
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.445272, total=  12.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.425949, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.450294, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.391765, total=  10.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.452386, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.316043, total=  11.0s
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.450733, total=  11.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.311915, total=  12.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.371386, total=  10.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.270995, total=  12.5s
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.417904, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.474198, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.520586, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.556583, total=  10.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.408729, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.505119, total=  12.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.482656, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.409140, total=  10.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.474956, total=  11.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.355497, total=  12.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.406017, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.482466, total=  10.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.492115, total=  11.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.337323, total=  11.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.488297, total=  11.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.390520, total=  10.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.415455, total=  13.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.445272, total=  12.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.446643, total=  12.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.450294, total=  10.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.425949, total=  11.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.450733, total=  11.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.452386, total=  10.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.391765, total=  10.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.311915, total=  13.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.316043, total=  11.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.371386, total=  10.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.417904, total=  10.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.270995, total=  12.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.474198, total=  10.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.520586, total=  11.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.556583, total=  11.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.426862, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.408729, total=  11.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.482656, total=  11.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.552955, total=   8.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.505119, total=  13.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.406017, total=  10.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.492717, total=   8.2s
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.474956, total=  11.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.409140, total=  11.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.492115, total=  11.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.488862, total=   8.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.355497, total=  12.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.482466, total=  11.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.415455, total=  12.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.502703, total=   8.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.445272, total=  12.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.446643, total=  13.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.401702, total=  10.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.542621, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.492394, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.412976, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.395929, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.385946, total=   8.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.422724, total=   9.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.461839, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.450083, total=   8.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.517841, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.528181, total=   8.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.452077, total=   7.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.538594, total=   8.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.618127, total=   9.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.482865, total=   8.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.450803, total=   8.1s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.469856, total=   8.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.507189, total=   8.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.430408, total=   7.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.524069, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.491122, total=  10.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.571958, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.523473, total=  10.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.501040, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.556461, total=   9.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.483966, total=   8.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.569620, total=  10.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.506463, total=   8.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.397447, total=   9.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.549038, total=   8.4s
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.499687, total=   7.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.405121, total=   7.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.384047, total=   7.3s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.415887, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.460602, total=   7.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.459887, total=   7.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.522644, total=   7.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.419548, total=   9.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.538529, total=   7.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.545259, total=   7.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.492750, total=   7.6s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.616989, total=   9.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.457554, total=   8.2s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.480369, total=   7.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.493043, total=   7.7s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.493767, total=   8.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.461434, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.428635, total=   7.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.529950, total=   8.1s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.525135, total=   9.5s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.552581, total=   8.8s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.569177, total=   6.9s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.580789, total=   9.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.491065, total=   7.0s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.502659, total=   7.4s
[CV] max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.528790, total=   7.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.556370, total=   7.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.497812, total=   6.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.430027, total=   6.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.413438, total=   6.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.393332, total=   6.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.393617, total=   8.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.465965, total=   6.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.462706, total=   6.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.406493, total=   7.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.522187, total=   7.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.530383, total=   7.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.554289, total=   7.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.470561, total=   6.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.620781, total=   8.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.483802, total=   7.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.477153, total=   7.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.482018, total=   6.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.500567, total=   8.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.509276, total=   7.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.520565, total=   8.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.544043, total=   7.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.538227, total=   7.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.585257, total=   8.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.420878, total=  17.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.465851, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.491144, total=  16.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.533951, total=  18.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.455954, total=  18.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.411936, total=  17.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.472385, total=  18.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.510999, total=  18.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.377107, total=  17.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.380882, total=  17.0s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.447607, total=  16.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.440807, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.504117, total=  17.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.377872, total=  21.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.515192, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.513223, total=  17.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.419901, total=  20.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.460845, total=  17.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.429256, total=  18.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.471939, total=  17.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.466828, total=  18.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.491122, total=  20.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.603337, total=  21.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.508932, total=  21.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.429078, total=  16.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.429088, total=  17.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.497059, total=  17.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.546698, total=  17.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.465434, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.494895, total=  16.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.476132, total=  17.5s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.532081, total=  17.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.483431, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.384110, total=  15.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.412560, total=  16.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.562547, total=  20.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.387846, total=  16.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.530074, total=  21.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.452970, total=  16.2s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.377872, total=  20.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.430399, total=  17.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.503431, total=  17.6s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.423077, total=  19.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.522468, total=  17.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.525980, total=  17.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.443177, total=  17.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.607129, total=  19.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.489989, total=  20.1s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.470285, total=  16.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.441933, total=  15.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.467487, total=  17.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.549710, total=  16.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.473330, total=  17.7s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.443565, total=  17.4s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.473757, total=  16.3s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.508822, total=  17.5s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.499687, total=  14.8s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.481518, total=  16.7s
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.546975, total=  15.9s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.507270, total=  20.0s
[CV] max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.419838, total=  14.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.487897, total=  16.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.391114, total=  14.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.525805, total=  20.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.572599, total=  20.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.394598, total=  15.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.461634, total=  14.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.390638, total=  19.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.427664, total=  17.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.445143, total=  16.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.508463, total=  15.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.616989, total=  18.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.537734, total=  15.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.521356, total=  16.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.442720, total=  16.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.461703, total=  15.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.478032, total=  16.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.458720, total=  15.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.469852, total=  16.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.519930, total=  15.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.486966, total=  18.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.500623, total=  18.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.571109, total=  18.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.520373, total=  19.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.399601, total=  36.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.427810, total=  37.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.392389, total=  34.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.445509, total=  35.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.385312, total=  38.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.466544, total=  38.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.418960, total=  39.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.319764, total=  36.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.441363, total=  39.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.315319, total=  45.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.430840, total=  42.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.331294, total=  37.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.407591, total=  37.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.378361, total=  38.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.469361, total=  36.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.376029, total=  38.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.411493, total=  37.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.367640, total=  39.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.406854, total=  39.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.381249, total=  39.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.474024, total=  44.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.406540, total=  40.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.395920, total=  45.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.436643, total=  49.2s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.365754, total=  39.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.392287, total=  35.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.389893, total=  37.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.400979, total=  37.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.435921, total=  39.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.422428, total=  39.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.465376, total=  43.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.415600, total=  45.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.487626, total=  37.3s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.387607, total=  35.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.340000, total=  45.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.439013, total=  38.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.450927, total=  36.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.314073, total=  35.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.413366, total=  35.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.343110, total=  37.7s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.371206, total=  38.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.361391, total=  37.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.415456, total=  37.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.479037, total=  36.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.430487, total=  45.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.364217, total=  37.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.483125, total=  45.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.388114, total=  37.5s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.391764, total=  45.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.379613, total=  38.6s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.397263, total=  37.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.375707, total=  38.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.388076, total=  33.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.477358, total=  44.0s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.418360, total=  33.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.427576, total=  36.8s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.448436, total=  35.1s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.447399, total=  34.9s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.406345, total=  35.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.434257, total=  32.4s
[CV] max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.475023, total=  34.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.465376, total=  42.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.382141, total=  31.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.390102, total=  31.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.460225, total=  46.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.358723, total=  43.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.416048, total=  33.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.350074, total=  34.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.349956, total=   8.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.370338, total=  33.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.466512, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.394682, total=   8.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.432604, total=  39.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.481763, total=   9.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.365532, total=  10.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.391583, total=  35.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.453428, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.427497, total=   9.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.447754, total=   9.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.394469, total=   8.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.320201, total=   8.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.359992, total=   8.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.398309, total=   8.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.269584, total=  10.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.437988, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.496452, total=  32.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.453797, total=   9.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.445203, total=  10.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.420740, total=   9.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.438793, total=  35.8s
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.483552, total=   9.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.472159, total=   9.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 






    



[Parallel(n_jobs=-1)]: Done 1520 tasks      | elapsed: 20.8min






    



[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.408612, total=   8.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.364941, total=   9.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.431882, total=   8.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.382246, total=  36.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.414657, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.516875, total=  40.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.506024, total=  10.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.425017, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.398506, total=  36.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.427162, total=  35.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.407004, total=  35.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.381588, total=  36.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.350407, total=  10.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.402457, total=  10.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.461991, total=  35.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.355940, total=   8.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.413676, total=  41.5s
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.491554, total=   8.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.439556, total=   9.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.462572, total=   9.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.422797, total=   8.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.390983, total=   9.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.369362, total=   9.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.459670, total=   8.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.445783, total=  41.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.445509, total=   9.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.388022, total=   8.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.313198, total=   8.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.362735, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.409653, total=   8.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.459792, total=  41.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=gini, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.467210, total=  42.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.273465, total=  10.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.449263, total=   9.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.465691, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.430427, total=   8.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.483767, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.473300, total=   8.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.431882, total=   8.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.400483, total=   9.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.450133, total=  10.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.426374, total=   9.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.414657, total=   9.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.450882, total=   9.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.373253, total=  11.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.485251, total=  11.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.349631, total=  10.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.358821, total=   9.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.408414, total=  10.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.492044, total=   8.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.409942, total=   9.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.469061, total=   9.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.462388, total=   8.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.383240, total=   8.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.418801, total=   9.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.441338, total=   9.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.340775, total=   8.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.368511, total=  10.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.362735, total=   8.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.268878, total=  10.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.396452, total=   9.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.455117, total=   9.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.484904, total=   9.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.471727, total=   8.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.426684, total=   9.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.473300, total=   9.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.431172, total=  10.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.408831, total=   9.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.431882, total=   9.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.415291, total=   8.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.417440, total=   8.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.377408, total=  11.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.518488, total=  10.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.444565, total=   9.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.398362, total=   9.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.359333, total=  10.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.361924, total=  22.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.421272, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.463036, total=  24.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.407756, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.480294, total=  25.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.474265, total=  23.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.426444, total=  23.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.271394, total=  23.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.395716, total=  23.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.403883, total=  22.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.419967, total=  22.9s
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.355319, total=  27.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.420642, total=  25.1s
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.465919, total=  24.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.432629, total=  24.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.473300, total=  24.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.433670, total=  24.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.291814, total=  27.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.408281, total=  23.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.462873, total=  24.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.430195, total=  23.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.486917, total=  28.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.498546, total=  26.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.378164, total=  28.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.403076, total=  23.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.359264, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.458864, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.399695, total=  25.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.429595, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.369322, total=  27.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.464871, total=  24.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.474891, total=  23.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.437133, total=  23.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.406286, total=  27.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.428506, total=  24.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.395716, total=  23.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.410846, total=  22.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.422649, total=  22.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.271394, total=  24.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.351064, total=  28.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.410451, total=  24.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.461802, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.436812, total=  23.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.439905, total=  23.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.480831, total=  23.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.291814, total=  28.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.486917, total=  28.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.380053, total=  27.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.445518, total=  23.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.402171, total=  23.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.406780, total=  24.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.363254, total=  22.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.436224, total=  24.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.410586, total=  23.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.462804, total=  23.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.420116, total=  25.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.512671, total=  28.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.369810, total=  27.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.480783, total=  24.6s
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.444888, total=  24.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.478225, total=  23.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.395924, total=  23.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.333771, total=  23.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.424152, total=  24.8s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.370067, total=  28.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.356596, total=  27.0s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.430487, total=  22.8s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.412956, total=  24.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.410451, total=  24.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.292167, total=  26.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.467292, total=  25.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.489571, total=  27.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.431308, total=  23.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.439475, total=  23.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.410641, total=  23.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.478092, total=  25.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.445518, total=  24.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.376275, total=  26.8s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.440863, total=  23.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.409061, total=  23.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.400814, total=  24.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.499792, total=  28.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.403958, total=  27.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.391288, total=  28.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.339317, total=  51.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.314949, total=  49.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.455156, total=  54.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.421857, total=  52.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.394125, total=  54.0s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.349364, total=  54.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.363901, total=  51.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.478641, total=  53.0s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.421632, total=  53.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.374974, total=  49.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.323404, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.373969, total=  50.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.381087, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.406982, total=  53.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.478957, total=  53.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.509687, total=  53.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.431090, total=  51.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.425527, total=  52.8s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.422410, total=  54.9s
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.387470, total=  52.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.452408, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.430891, total=  55.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.390253, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.414209, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.405112, total=  53.0s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.339317, total=  51.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.467654, total=  52.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.455156, total=  53.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.394125, total=  54.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.349364, total=  56.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.421857, total=  53.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.434226, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.478641, total=  51.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.421632, total=  53.8s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.323404, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=1, score=0.451973, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.363901, total=  51.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.314949, total=  49.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.373969, total=  51.2s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.374974, total=  52.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.406982, total=  53.0s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.381087, total=  59.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.478957, total=  54.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.431090, total=  53.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.509687, total=  54.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.452408, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.422410, total=  53.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.390253, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.425527, total=  54.3s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.387470, total=  53.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.430891, total=  52.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.339317, total=  49.5s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.405112, total=  54.6s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.467654, total=  52.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.414209, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.349364, total=  53.1s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.394125, total=  53.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.455156, total=  54.9s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.434226, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.421857, total=  54.7s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=3, score=0.451973, total= 1.0min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.421632, total=  52.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.363901, total=  50.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.478641, total=  53.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.314949, total=  49.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.323404, total= 1.1min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.374974, total=  51.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.373969, total=  51.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.406982, total=  52.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.381087, total= 1.0min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.478957, total=  53.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.423980, total=  26.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.548783, total=  27.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.484393, total=  27.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.480539, total=  28.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.452408, total= 1.0min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.503643, total=  28.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.392340, total=  32.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.431090, total=  54.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.509687, total=  56.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.481350, total=  26.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.422410, total=  55.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.408401, total=  25.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.540101, total=  28.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.397461, total=  27.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.425527, total=  54.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.387470, total=  53.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.408257, total=  31.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.430891, total=  53.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.405112, total=  54.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.467654, total=  52.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.390253, total= 1.0min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.414209, total= 1.0min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.385735, total=  27.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.455033, total=  26.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.452298, total=  27.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.434226, total= 1.1min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.520128, total=  27.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=3, min_samples_leaf=10, score=0.451973, total= 1.1min
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.529723, total=  28.8s
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.541174, total=  27.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.466454, total=  28.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.610922, total=  32.3s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.474297, total=  29.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.467496, total=  29.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.503711, total=  28.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.471839, total=  28.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.530536, total=  31.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.489989, total=  34.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.431516, total=  25.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.516663, total=  28.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.554809, total=  26.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.552581, total=  33.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.574832, total=  32.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.496879, total=  26.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.489596, total=  27.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.520094, total=  27.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.545371, total=  27.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.493853, total=  25.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.402553, total=  31.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.398774, total=  25.4s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.409441, total=  25.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.383414, total=  25.6s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.457096, total=  26.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.452949, total=  26.5s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.417431, total=  30.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.530878, total=  27.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.543109, total=  27.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.522897, total=  27.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.463487, total=  26.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.624573, total=  31.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.483587, total=  26.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.476274, total=  28.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.507653, total=  26.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.484700, total=  30.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.479077, total=  27.2s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.529079, total=  27.0s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.436835, total=  24.1s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.558980, total=  24.8s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.536352, total=  32.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.565386, total=  31.7s
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.579300, total=  31.7s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.495226, total=  23.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.492254, total=  25.9s
[CV] max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.530435, total=  25.1s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.397872, total=  29.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.404027, total=  22.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.500313, total=  23.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.419214, total=  23.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.563932, total=  25.4s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.387635, total=  23.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.459983, total=  23.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.459020, total=  24.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.418137, total=  28.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.523788, total=  24.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.523778, total=  24.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.541819, total=  23.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.465997, total=  25.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.481327, total=  25.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.621540, total=  29.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.485304, total=  25.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.492633, total=  28.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.480660, total=  24.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.495362, total=  25.3s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.543237, total=  26.5s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.523058, total=  29.2s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.565897, total=  28.9s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=1, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.555685, total=  30.6s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.426197, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.473929, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.534878, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.483017, total= 1.0min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.379733, total=  60.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.414223, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.458035, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.472385, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.512603, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.380249, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.454208, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.434952, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.505718, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.504624, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.437471, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.368511, total= 1.4min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.429781, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.459773, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.458699, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.519673, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.474026, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.602958, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.484322, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.526381, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.464148, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.431959, total= 1.0min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.474663, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.495520, total= 1.0min
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.542063, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.491832, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.458035, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.418382, total= 1.0min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.382578, total= 1.0min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.522456, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.382781, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.484841, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.452970, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.528133, total= 1.4min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.567014, total= 1.4min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.440373, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.508920, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.377021, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.426958, total= 1.2min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.510568, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.524618, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.437928, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.604096, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.472988, total= 1.3min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.468998, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.465290, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.441046, total=  59.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.467996, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.468220, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.499062, total=  55.8s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.493357, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.543453, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.410896, total=  57.7s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.498898, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.469133, total= 1.1min
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.395491, total=  56.0s
[CV] max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.534372, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.487427, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.382570, total=  57.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.520980, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.569993, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.464109, total=  58.4s
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.526581, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.390213, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.444709, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.423077, total= 1.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.513952, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.608646, total= 1.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.510788, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.529564, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.448425, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.471573, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.465070, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.471475, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.466637, total= 1.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.507079, total= 1.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.472611, total= 1.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.520150, total= 1.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.531238, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=3, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.567759, total= 1.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.384752, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.370763, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.314073, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.440788, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.383830, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.401387, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.423501, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.440422, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.413396, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.321587, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.404290, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.358630, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.306383, total= 3.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.398024, total= 2.8min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.409881, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.353948, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.404429, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.395200, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.393478, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.365334, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.371058, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.470990, total= 2.8min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.415187, total= 2.9min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.449938, total= 2.9min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.372340, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.379552, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.441715, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.442387, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.406243, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.439657, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.368268, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.440432, total= 2.9min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.392999, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.322828, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.448442, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.397415, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=1, score=0.434614, total= 3.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.319477, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.310213, total= 2.9min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.417904, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.410110, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.359280, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.398024, total= 2.7min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.434100, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.387054, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.362848, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.473265, total= 2.8min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.456366, total= 2.8min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.383180, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.368629, total= 2.5min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.392161, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.413029, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.389406, total= 2.1min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.416157, total= 2.2min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.481344, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.421272, total= 2.3min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.452625, total= 2.4min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.481097, total= 2.8min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.394261, total= 2.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.417170, total= 2.0min
[CV] max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10 
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.478460, total= 2.3min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.376669, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.424525, total= 3.0min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.413161, total= 2.4min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.331064, total= 2.7min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=3, score=0.433358, total= 2.9min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.316100, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.427393, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.412844, total= 2.5min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.411752, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.415599, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.404668, total= 2.0min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.377681, total= 1.9min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.450226, total= 2.0min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.493743, total= 2.3min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.419223, total= 1.8min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.378076, total= 1.9min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.401206, total= 1.8min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.392445, total= 1.7min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.437854, total= 2.2min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.455021, total= 1.8min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.476527, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.439659, total= 2.1min
[CV]  max_features=7, criterion=entropy, bootstrap=False, max_depth=None, min_samples_leaf=10, score=0.456813, total= 2.0min






    



[Parallel(n_jobs=-1)]: Done 2016 out of 2016 | elapsed: 40.9min finished



In [32]:

    
pd.DataFrame(clf.cv_results_).sort_values(by="rank_test_score").head()









    Out[32]:






  
    
      
      mean_fit_time
      mean_score_time
      mean_test_score
      mean_train_score
      param_bootstrap
      param_criterion
      param_max_depth
      param_max_features
      param_min_samples_leaf
      params
      ...
      split7_test_score
      split7_train_score
      split8_test_score
      split8_train_score
      split9_test_score
      split9_train_score
      std_fit_time
      std_score_time
      std_test_score
      std_train_score
    
  
  
    
      11
      4.558404
      0.225488
      0.496092
      0.890204
      True
      gini
      None
      1
      10
      {'max_features': 1, 'criterion': 'gini', 'max_...
      ...
      0.501146
      0.894286
      0.440632
      0.889331
      0.401620
      0.896564
      0.397693
      0.047657
      0.054994
      0.006068
    
    
      29
      15.797385
      0.242615
      0.493505
      0.893064
      True
      entropy
      None
      1
      10
      {'max_features': 1, 'criterion': 'entropy', 'm...
      ...
      0.498229
      0.891460
      0.438345
      0.892778
      0.400306
      0.900113
      1.739411
      0.061737
      0.054174
      0.005962
    
    
      47
      7.021959
      0.230678
      0.493328
      0.931486
      False
      gini
      None
      1
      10
      {'max_features': 1, 'criterion': 'gini', 'max_...
      ...
      0.497812
      0.932615
      0.430027
      0.928578
      0.413438
      0.936986
      0.632567
      0.045544
      0.054194
      0.004293
    
    
      10
      5.130352
      0.238740
      0.492450
      0.973722
      True
      gini
      None
      1
      3
      {'max_features': 1, 'criterion': 'gini', 'max_...
      ...
      0.505522
      0.976155
      0.418590
      0.972686
      0.396805
      0.976024
      0.407751
      0.038363
      0.054357
      0.001549
    
    
      65
      25.445620
      0.245247
      0.491015
      0.934640
      False
      entropy
      None
      1
      10
      {'max_features': 1, 'criterion': 'entropy', 'm...
      ...
      0.500313
      0.934823
      0.419214
      0.930788
      0.404027
      0.940708
      2.282686
      0.068402
      0.054038
      0.004525
    
  

5 rows × 71 columns



In [33]:

    
clf_rf = RandomForestClassifier(n_estimators=200, max_depth=None, max_features=1, min_samples_leaf=10, bootstrap=False, 
                                criterion="gini")

clf_rf.fit(X_train, y_train)
predicted_labels = clf_rf.predict(X_test)

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS   116    15     3                 1                     135
     CSiS         318    24                 1                     343
     FSiS          34   253     1           1                     289
     SiSh           3     9    86     1     4     1     2         106
       MS           1     2     6    78    19     1     3         110
       WS           1     1     4     2   205     5    11         229
        D                 2     1     1    18    46    12     2    82
       PS                 1     1     2    11     3   208     2   228
       BS                             1     2     2    17    69    91

Precision  1.00  0.85  0.86  0.87  0.92  0.78  0.79  0.82  0.95  0.86
   Recall  0.86  0.93  0.88  0.81  0.71  0.90  0.56  0.91  0.76  0.85
       F1  0.92  0.89  0.87  0.84  0.80  0.84  0.66  0.86  0.84  0.85

Nearest Neighbours classifier

This classifer has two parameters that can be tuned: the number of neighbours to use, and the method of weighting the neighbours (uniform or distance based). We'll use GridSearchCV again to find the optimal set.



In [34]:

    
from sklearn import neighbors

parameters = {'n_neighbors': np.arange(1, 25), 'weights': ['uniform', 'distance']}
knn = neighbors.KNeighborsClassifier()
clf = LPWO_CV(knn, parameters)









    



Fitting 28 folds for each of 48 candidates, totalling 1344 fits
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] ... weights=uniform, n_neighbors=1, score=0.317872, total=   0.1s
[CV] ... weights=uniform, n_neighbors=1, score=0.358378, total=   0.1s
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] ... weights=uniform, n_neighbors=1, score=0.416918, total=   0.2s
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] ... weights=uniform, n_neighbors=1, score=0.387514, total=   0.1s
[CV] ... weights=uniform, n_neighbors=1, score=0.457149, total=   0.2s
[CV] weights=uniform, n_neighbors=1 ..................................
[CV] ... weights=uniform, n_neighbors=1, score=0.365914, total=   0.1s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.433048, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.382580, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.384489, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.339156, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.336297, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.477436, total=   0.1s
[CV] ... weights=uniform, n_neighbors=1, score=0.344714, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.305761, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.388614, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.402562, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.392058, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.395200, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.479020, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.376098, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.424210, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.427805, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.406308, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.418587, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.501489, total=   0.1s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.397421, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] ... weights=uniform, n_neighbors=1, score=0.438106, total=   0.2s
[CV] ... weights=uniform, n_neighbors=1, score=0.421259, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.358378, total=   0.2s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] .. weights=distance, n_neighbors=1, score=0.416918, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.387514, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.317872, total=   0.1s
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] weights=distance, n_neighbors=1 .................................
[CV] .. weights=distance, n_neighbors=1, score=0.433048, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.384489, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.365914, total=   0.1s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.457149, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.339156, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.344714, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.477436, total=   0.1s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.388614, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.305761, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.424210, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.402562, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.418587, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.392058, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.336297, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.382580, total=   0.3s
[CV] .. weights=distance, n_neighbors=1, score=0.427805, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.395200, total=   0.3s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.438106, total=   0.1s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.376098, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.406308, total=   0.2s
[CV] .. weights=distance, n_neighbors=1, score=0.397421, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.479020, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.421259, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] .. weights=distance, n_neighbors=1, score=0.501489, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] ... weights=uniform, n_neighbors=2, score=0.409502, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] ... weights=uniform, n_neighbors=2, score=0.384509, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.459441, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.357048, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] ... weights=uniform, n_neighbors=2, score=0.308511, total=   0.1s
[CV] ... weights=uniform, n_neighbors=2, score=0.376968, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.438923, total=   0.2s
[CV] weights=uniform, n_neighbors=2 ..................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.356314, total=   0.3s
[CV] ... weights=uniform, n_neighbors=2, score=0.461509, total=   0.1s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.333478, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.397896, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.299641, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.360621, total=   0.1s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.388375, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.370606, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.407365, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.381329, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.338324, total=   0.3s
[CV] ... weights=uniform, n_neighbors=2, score=0.484005, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.402328, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.428878, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.431950, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.419136, total=   0.3s
[CV] ... weights=uniform, n_neighbors=2, score=0.405365, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.426269, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.423749, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] .. weights=distance, n_neighbors=2, score=0.416918, total=   0.2s
[CV] ... weights=uniform, n_neighbors=2, score=0.418135, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.433048, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=distance, n_neighbors=2 .................................
[CV] .. weights=distance, n_neighbors=2, score=0.384489, total=   0.2s
[CV] .. weights=distance, n_neighbors=2, score=0.317872, total=   0.2s
[CV] weights=distance, n_neighbors=2 .................................
[CV] .. weights=distance, n_neighbors=2, score=0.364855, total=   0.2s
[CV] .. weights=distance, n_neighbors=2, score=0.358378, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] ... weights=uniform, n_neighbors=2, score=0.501117, total=   0.3s
[CV] weights=distance, n_neighbors=2 .................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.339156, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.382580, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.344714, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.305761, total=   0.3s






    



[Parallel(n_jobs=-1)]: Done  80 tasks      | elapsed:    1.5s






    



[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.387514, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.388614, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.457149, total=   0.4s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.476299, total=   0.2s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.336513, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.424210, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.417832, total=   0.2s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.376098, total=   0.2s
[CV] .. weights=distance, n_neighbors=2, score=0.395200, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.402562, total=   0.4s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.478189, total=   0.1s
[CV] .. weights=distance, n_neighbors=2, score=0.437718, total=   0.1s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.421259, total=   0.2s
[CV] .. weights=distance, n_neighbors=2, score=0.392287, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.427805, total=   0.3s
[CV] .. weights=distance, n_neighbors=2, score=0.406308, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.395838, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.426883, total=   0.2s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.397421, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] ... weights=uniform, n_neighbors=3, score=0.363032, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] .. weights=distance, n_neighbors=2, score=0.500745, total=   0.2s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] ... weights=uniform, n_neighbors=3, score=0.467003, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.392999, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.323404, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.447491, total=   0.3s
[CV] weights=uniform, n_neighbors=3 ..................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.341651, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.394125, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.305972, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.396658, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.362738, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.359160, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.487675, total=   0.1s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.347572, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.447001, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.408851, total=   0.2s
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.416972, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.419720, total=   0.2s
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.514892, total=   0.1s
[CV] ... weights=uniform, n_neighbors=3, score=0.439659, total=   0.1s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.379394, total=   0.3s
[CV] ... weights=uniform, n_neighbors=3, score=0.413966, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.420267, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.429035, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.489821, total=   0.2s
[CV] ... weights=uniform, n_neighbors=3, score=0.438533, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] ... weights=uniform, n_neighbors=3, score=0.431496, total=   0.2s
[CV] weights=distance, n_neighbors=3 .................................
[CV] .. weights=distance, n_neighbors=3, score=0.393526, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.425724, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.324255, total=   0.2s
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] weights=distance, n_neighbors=3 .................................
[CV] .. weights=distance, n_neighbors=3, score=0.446756, total=   0.3s
[CV] weights=distance, n_neighbors=3 .................................
[CV] .. weights=distance, n_neighbors=3, score=0.468378, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.363032, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.395535, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.389039, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.339988, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.355877, total=   0.2s
[CV] .. weights=distance, n_neighbors=3, score=0.485021, total=   0.1s
[CV] .. weights=distance, n_neighbors=3, score=0.364502, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.344753, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.396865, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.415142, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.405548, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.444206, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.306394, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.412597, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.419720, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.379613, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.437889, total=   0.3s
[CV] .. weights=distance, n_neighbors=3, score=0.486913, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.438882, total=   0.1s
[CV] .. weights=distance, n_neighbors=3, score=0.416874, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.516754, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.426020, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] .. weights=distance, n_neighbors=3, score=0.431061, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] ... weights=uniform, n_neighbors=4, score=0.394682, total=   0.2s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] ... weights=uniform, n_neighbors=4, score=0.443329, total=   0.2s
[CV] ... weights=uniform, n_neighbors=4, score=0.316170, total=   0.1s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] ... weights=uniform, n_neighbors=4, score=0.365027, total=   0.3s
[CV] weights=uniform, n_neighbors=4 ..................................
[CV] ... weights=uniform, n_neighbors=4, score=0.395300, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.469065, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.416454, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.365208, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.344146, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.306816, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.357846, total=   0.2s
[CV] ... weights=uniform, n_neighbors=4, score=0.390290, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.344753, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.475540, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.414456, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.403126, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.413738, total=   0.3s
[CV] ... weights=uniform, n_neighbors=4, score=0.453021, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.480681, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.420476, total=   0.1s
[CV] ... weights=uniform, n_neighbors=4, score=0.421850, total=   0.2s
[CV] ... weights=uniform, n_neighbors=4, score=0.442609, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.382689, total=   0.3s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.405734, total=   0.4s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.428571, total=   0.2s
[CV] ... weights=uniform, n_neighbors=4, score=0.437813, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.441211, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] .. weights=distance, n_neighbors=4, score=0.431518, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] ... weights=uniform, n_neighbors=4, score=0.514892, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] .. weights=distance, n_neighbors=4, score=0.453366, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.325957, total=   0.1s
[CV] .. weights=distance, n_neighbors=4, score=0.395770, total=   0.2s
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] weights=distance, n_neighbors=4 .................................
[CV] .. weights=distance, n_neighbors=4, score=0.396069, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.364583, total=   0.4s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.392373, total=   0.3s
[CV] .. weights=distance, n_neighbors=4, score=0.470440, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.352559, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.364502, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.398102, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.360692, total=   0.3s
[CV] .. weights=distance, n_neighbors=4, score=0.341443, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.421317, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.411933, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.492605, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.448721, total=   0.3s
[CV] .. weights=distance, n_neighbors=4, score=0.416705, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.424254, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.312302, total=   0.4s
[CV] .. weights=distance, n_neighbors=4, score=0.441751, total=   0.3s
[CV] .. weights=distance, n_neighbors=4, score=0.384007, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.499377, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.445867, total=   0.2s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.516754, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.432050, total=   0.2s
[CV] .. weights=distance, n_neighbors=4, score=0.434328, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] ... weights=uniform, n_neighbors=5, score=0.366356, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] ... weights=uniform, n_neighbors=5, score=0.429200, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] .. weights=distance, n_neighbors=4, score=0.422529, total=   0.4s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] ... weights=uniform, n_neighbors=5, score=0.448470, total=   0.2s
[CV] ... weights=uniform, n_neighbors=5, score=0.403237, total=   0.3s
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] weights=uniform, n_neighbors=5 ..................................
[CV] ... weights=uniform, n_neighbors=5, score=0.328085, total=   0.2s
[CV] ... weights=uniform, n_neighbors=5, score=0.475940, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.403055, total=   0.2s
[CV] ... weights=uniform, n_neighbors=5, score=0.370148, total=   0.2s
[CV] ... weights=uniform, n_neighbors=5, score=0.348097, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.356461, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.398208, total=   0.4s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.366382, total=   0.3s
[CV] ... weights=uniform, n_neighbors=5, score=0.411304, total=   0.3s
[CV] ... weights=uniform, n_neighbors=5, score=0.460546, total=   0.3s
[CV] ... weights=uniform, n_neighbors=5, score=0.493743, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.419259, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.314834, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.418758, total=   0.4s
[CV] ... weights=uniform, n_neighbors=5, score=0.429165, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.391696, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.451834, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.415107, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................






    



[Parallel(n_jobs=-1)]: Done 240 tasks      | elapsed:    4.7s






    



[CV] ... weights=uniform, n_neighbors=5, score=0.454016, total=   0.1s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.487329, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.435529, total=   0.3s
[CV] ... weights=uniform, n_neighbors=5, score=0.443694, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] .. weights=distance, n_neighbors=5, score=0.328936, total=   0.1s
[CV] ... weights=uniform, n_neighbors=5, score=0.425469, total=   0.3s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] .. weights=distance, n_neighbors=5, score=0.428969, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] .. weights=distance, n_neighbors=5, score=0.401387, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] weights=distance, n_neighbors=5 .................................
[CV] .. weights=distance, n_neighbors=5, score=0.366135, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] .. weights=distance, n_neighbors=5, score=0.449449, total=   0.2s
[CV] weights=distance, n_neighbors=5 .................................
[CV] ... weights=uniform, n_neighbors=5, score=0.528667, total=   0.2s
[CV] .. weights=distance, n_neighbors=5, score=0.403055, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.366382, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.394249, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.409035, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.476398, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.314623, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.370854, total=   0.2s
[CV] .. weights=distance, n_neighbors=5, score=0.358196, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.492226, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.348097, total=   0.4s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.415792, total=   0.2s
[CV] .. weights=distance, n_neighbors=5, score=0.421089, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.415236, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.456461, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.430676, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.529412, total=   0.1s
[CV] .. weights=distance, n_neighbors=5, score=0.392355, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.452852, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.448187, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.434137, total=   0.3s
[CV] .. weights=distance, n_neighbors=5, score=0.490237, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] ... weights=uniform, n_neighbors=6, score=0.407756, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] ... weights=uniform, n_neighbors=6, score=0.448470, total=   0.2s
[CV] .. weights=distance, n_neighbors=5, score=0.441952, total=   0.3s
[CV] ... weights=uniform, n_neighbors=6, score=0.400694, total=   0.3s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] ... weights=uniform, n_neighbors=6, score=0.325532, total=   0.1s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] .. weights=distance, n_neighbors=5, score=0.424112, total=   0.3s
[CV] ... weights=uniform, n_neighbors=6, score=0.476169, total=   0.2s
[CV] weights=uniform, n_neighbors=6 ..................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.374113, total=   0.4s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.427346, total=   0.4s
[CV] ... weights=uniform, n_neighbors=6, score=0.370759, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.382145, total=   0.2s
[CV] ... weights=uniform, n_neighbors=6, score=0.351632, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.322853, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.491847, total=   0.1s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.406960, total=   0.4s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.414795, total=   0.3s
[CV] ... weights=uniform, n_neighbors=6, score=0.420098, total=   0.2s
[CV] ... weights=uniform, n_neighbors=6, score=0.356678, total=   0.4s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.421775, total=   0.4s
[CV] ... weights=uniform, n_neighbors=6, score=0.450976, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.417492, total=   0.4s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.411000, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.386643, total=   0.3s
[CV] ... weights=uniform, n_neighbors=6, score=0.466566, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.483174, total=   0.2s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.435993, total=   0.2s
[CV] ... weights=uniform, n_neighbors=6, score=0.445655, total=   0.2s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.520477, total=   0.2s
[CV] weights=distance, n_neighbors=6 .................................
[CV] .. weights=distance, n_neighbors=6, score=0.404162, total=   0.2s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.451688, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] .. weights=distance, n_neighbors=6, score=0.433835, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] .. weights=distance, n_neighbors=6, score=0.368794, total=   0.3s
[CV] .. weights=distance, n_neighbors=6, score=0.332766, total=   0.2s
[CV] weights=distance, n_neighbors=6 .................................
[CV] ... weights=uniform, n_neighbors=6, score=0.423660, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] .. weights=distance, n_neighbors=6, score=0.450673, total=   0.3s
[CV] weights=distance, n_neighbors=6 .................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.370501, total=   0.1s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.405875, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.477314, total=   0.3s
[CV] .. weights=distance, n_neighbors=6, score=0.499810, total=   0.2s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.424520, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.362005, total=   0.3s
[CV] .. weights=distance, n_neighbors=6, score=0.460761, total=   0.3s
[CV] .. weights=distance, n_neighbors=6, score=0.348513, total=   0.4s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.400917, total=   0.4s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.407384, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.425009, total=   0.2s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.421180, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.391037, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.358846, total=   0.3s
[CV] .. weights=distance, n_neighbors=6, score=0.319477, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.413510, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.435297, total=   0.2s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.490237, total=   0.2s
[CV] .. weights=distance, n_neighbors=6, score=0.449045, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.424112, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.455180, total=   0.2s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.524944, total=   0.1s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] .. weights=distance, n_neighbors=6, score=0.443476, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] ... weights=uniform, n_neighbors=7, score=0.431750, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.376551, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] ... weights=uniform, n_neighbors=7, score=0.414336, total=   0.2s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] ... weights=uniform, n_neighbors=7, score=0.331915, total=   0.2s
[CV] ... weights=uniform, n_neighbors=7, score=0.454100, total=   0.3s
[CV] weights=uniform, n_neighbors=7 ..................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.407399, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.324119, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.482814, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.354336, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.420173, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.370103, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.367953, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.502086, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.423162, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.380028, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.424291, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.475382, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.406960, total=   0.4s
[CV] ... weights=uniform, n_neighbors=7, score=0.430298, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.418302, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.396309, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.456125, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.438312, total=   0.3s
[CV] ... weights=uniform, n_neighbors=7, score=0.425922, total=   0.3s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.460613, total=   0.2s
[CV] ... weights=uniform, n_neighbors=7, score=0.486498, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] .. weights=distance, n_neighbors=7, score=0.372562, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.405780, total=   0.2s
[CV] .. weights=distance, n_neighbors=7, score=0.414336, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] ... weights=uniform, n_neighbors=7, score=0.453496, total=   0.4s
[CV] ... weights=uniform, n_neighbors=7, score=0.526806, total=   0.2s
[CV] weights=distance, n_neighbors=7 .................................
[CV] .. weights=distance, n_neighbors=7, score=0.481210, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.350177, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.405918, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.433372, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=distance, n_neighbors=7 .................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.369884, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.333191, total=   0.2s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.416667, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.457038, total=   0.4s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.327284, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.377911, total=   0.2s
[CV] .. weights=distance, n_neighbors=7, score=0.365568, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.428408, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.395650, total=   0.2s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.473662, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.430676, total=   0.2s
[CV] .. weights=distance, n_neighbors=7, score=0.503603, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.424923, total=   0.4s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.453765, total=   0.2s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.423094, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.463718, total=   0.1s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.441790, total=   0.2s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.495222, total=   0.2s
[CV] .. weights=distance, n_neighbors=7, score=0.428636, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] .. weights=distance, n_neighbors=7, score=0.453060, total=   0.3s
[CV] .. weights=distance, n_neighbors=7, score=0.529412, total=   0.2s
[CV] ... weights=uniform, n_neighbors=8, score=0.406705, total=   0.3s
[CV] ... weights=uniform, n_neighbors=8, score=0.382757, total=   0.3s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] ... weights=uniform, n_neighbors=8, score=0.329362, total=   0.1s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] ... weights=uniform, n_neighbors=8, score=0.412456, total=   0.2s
[CV] weights=uniform, n_neighbors=8 ..................................
[CV] ... weights=uniform, n_neighbors=8, score=0.425724, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.413003, total=   0.4s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.476627, total=   0.4s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.449939, total=   0.4s
[CV] ... weights=uniform, n_neighbors=8, score=0.364109, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.426568, total=   0.2s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.393084, total=   0.2s
[CV] ... weights=uniform, n_neighbors=8, score=0.377982, total=   0.3s
[CV] ... weights=uniform, n_neighbors=8, score=0.331505, total=   0.3s
[CV] ... weights=uniform, n_neighbors=8, score=0.491847, total=   0.2s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.358846, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.424062, total=   0.3s
[CV] ... weights=uniform, n_neighbors=8, score=0.414575, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.471727, total=   0.4s
[CV] ... weights=uniform, n_neighbors=8, score=0.397408, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.452907, total=   0.4s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.485667, total=   0.2s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.456733, total=   0.2s
[CV] ... weights=uniform, n_neighbors=8, score=0.429165, total=   0.4s
[CV] ... weights=uniform, n_neighbors=8, score=0.447615, total=   0.3s
[CV] ... weights=uniform, n_neighbors=8, score=0.441790, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.418759, total=   0.5s
[CV] weights=distance, n_neighbors=8 .................................
[CV] .. weights=distance, n_neighbors=8, score=0.434531, total=   0.2s
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.515637, total=   0.3s
[CV] .. weights=distance, n_neighbors=8, score=0.377881, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] ... weights=uniform, n_neighbors=8, score=0.420041, total=   0.4s
[CV] weights=distance, n_neighbors=8 .................................
[CV] .. weights=distance, n_neighbors=8, score=0.337447, total=   0.2s
[CV] .. weights=distance, n_neighbors=8, score=0.409942, total=   0.3s
[CV] weights=distance, n_neighbors=8 .................................
[CV] weights=distance, n_neighbors=8 .................................
[CV] .. weights=distance, n_neighbors=8, score=0.456059, total=   0.4s
[CV] .. weights=distance, n_neighbors=8, score=0.483731, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.417391, total=   0.2s
[CV] .. weights=distance, n_neighbors=8, score=0.355375, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.367914, total=   0.4s
[CV] .. weights=distance, n_neighbors=8, score=0.407585, total=   0.4s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.381793, total=   0.2s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.361448, total=   0.3s
[CV] .. weights=distance, n_neighbors=8, score=0.434454, total=   0.2s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.503223, total=   0.2s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.400483, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.422182, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.328972, total=   0.3s
[CV] .. weights=distance, n_neighbors=8, score=0.424977, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.487744, total=   0.2s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.424483, total=   0.4s
[CV] .. weights=distance, n_neighbors=8, score=0.423660, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.465270, total=   0.2s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.418523, total=   0.4s
[CV] .. weights=distance, n_neighbors=8, score=0.525316, total=   0.2s
[CV] .. weights=distance, n_neighbors=8, score=0.456340, total=   0.4s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] .. weights=distance, n_neighbors=8, score=0.440399, total=   0.3s
[CV] .. weights=distance, n_neighbors=8, score=0.472587, total=   0.5s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] ... weights=uniform, n_neighbors=9, score=0.427578, total=   0.3s
[CV] .. weights=distance, n_neighbors=8, score=0.452843, total=   0.4s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] ... weights=uniform, n_neighbors=9, score=0.453611, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] ... weights=uniform, n_neighbors=9, score=0.378324, total=   0.3s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] ... weights=uniform, n_neighbors=9, score=0.407630, total=   0.4s
[CV] weights=uniform, n_neighbors=9 ..................................
[CV] ... weights=uniform, n_neighbors=9, score=0.412691, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.334043, total=   0.2s
[CV] ... weights=uniform, n_neighbors=9, score=0.480752, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.371197, total=   0.3s
[CV] ... weights=uniform, n_neighbors=9, score=0.360782, total=   0.4s
[CV] ... weights=uniform, n_neighbors=9, score=0.426349, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.414461, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.384968, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.363833, total=   0.4s
[CV] ... weights=uniform, n_neighbors=9, score=0.498294, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.420173, total=   0.4s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.422941, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.329394, total=   0.4s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.422638, total=   0.3s
[CV] ... weights=uniform, n_neighbors=9, score=0.472372, total=   0.3s
[CV] ... weights=uniform, n_neighbors=9, score=0.453765, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.403559, total=   0.3s
[CV] ... weights=uniform, n_neighbors=9, score=0.483590, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.440631, total=   0.3s






    



[Parallel(n_jobs=-1)]: Done 464 tasks      | elapsed:   11.0s






    



[CV] ... weights=uniform, n_neighbors=9, score=0.461777, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.432187, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.451536, total=   0.3s
[CV] ... weights=uniform, n_neighbors=9, score=0.422076, total=   0.4s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=distance, n_neighbors=9 .................................
[CV] ... weights=uniform, n_neighbors=9, score=0.518615, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.410173, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.431981, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.339149, total=   0.2s
[CV] .. weights=distance, n_neighbors=9, score=0.483043, total=   0.2s
[CV] weights=distance, n_neighbors=9 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.380541, total=   0.3s
[CV] weights=distance, n_neighbors=9 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.416216, total=   0.4s
[CV] .. weights=distance, n_neighbors=9, score=0.456304, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.368571, total=   0.3s
[CV] .. weights=distance, n_neighbors=9, score=0.356415, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.416048, total=   0.3s
[CV] .. weights=distance, n_neighbors=9, score=0.410085, total=   0.5s
[CV] .. weights=distance, n_neighbors=9, score=0.363400, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.331083, total=   0.3s
[CV] .. weights=distance, n_neighbors=9, score=0.382145, total=   0.2s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.499810, total=   0.2s
[CV] .. weights=distance, n_neighbors=9, score=0.420960, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.472372, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.432943, total=   0.2s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.401582, total=   0.3s
[CV] .. weights=distance, n_neighbors=9, score=0.489821, total=   0.2s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.426578, total=   0.4s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.423323, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.442022, total=   0.4s
[CV] .. weights=distance, n_neighbors=9, score=0.422529, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.452263, total=   0.4s
[CV] .. weights=distance, n_neighbors=9, score=0.521221, total=   0.2s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.466822, total=   0.2s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=distance, n_neighbors=9, score=0.449575, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=uniform, n_neighbors=10, score=0.407630, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=uniform, n_neighbors=10, score=0.385417, total=   0.4s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=uniform, n_neighbors=10, score=0.330638, total=   0.2s
[CV] .. weights=uniform, n_neighbors=10, score=0.422480, total=   0.3s
[CV] weights=uniform, n_neighbors=10 .................................
[CV] weights=uniform, n_neighbors=10 .................................
[CV] .. weights=uniform, n_neighbors=10, score=0.415503, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.394143, total=   0.2s
[CV] .. weights=uniform, n_neighbors=10, score=0.478918, total=   0.3s
[CV] .. weights=uniform, n_neighbors=10, score=0.452387, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.425124, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.377325, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.367852, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.412691, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.422004, total=   0.3s
[CV] .. weights=uniform, n_neighbors=10, score=0.332349, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.356245, total=   0.4s
[CV] .. weights=uniform, n_neighbors=10, score=0.416557, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.471942, total=   0.4s
[CV] .. weights=uniform, n_neighbors=10, score=0.428343, total=   0.3s
[CV] .. weights=uniform, n_neighbors=10, score=0.488813, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.454838, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.433321, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.405756, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.485667, total=   0.2s
[CV] .. weights=uniform, n_neighbors=10, score=0.448052, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.417326, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.461777, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.432445, total=   0.2s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] .. weights=uniform, n_neighbors=10, score=0.515637, total=   0.2s
[CV] weights=distance, n_neighbors=10 ................................
[CV] . weights=distance, n_neighbors=10, score=0.414566, total=   0.3s
[CV] .. weights=uniform, n_neighbors=10, score=0.451536, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] weights=distance, n_neighbors=10 ................................
[CV] . weights=distance, n_neighbors=10, score=0.382092, total=   0.4s
[CV] weights=distance, n_neighbors=10 ................................
[CV] . weights=distance, n_neighbors=10, score=0.456793, total=   0.3s
[CV] weights=distance, n_neighbors=10 ................................
[CV] . weights=distance, n_neighbors=10, score=0.419271, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.367695, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.485564, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.339149, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.384263, total=   0.2s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.361015, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.409669, total=   0.5s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.331294, total=   0.4s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.422919, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.494881, total=   0.2s
[CV] . weights=distance, n_neighbors=10, score=0.417079, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.362653, total=   0.4s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.439365, total=   0.2s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.470651, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.419199, total=   0.4s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.426061, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.407733, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.443414, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.467210, total=   0.2s
[CV] . weights=distance, n_neighbors=10, score=0.488160, total=   0.2s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.421850, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.519732, total=   0.3s
[CV] . weights=distance, n_neighbors=10, score=0.455482, total=   0.5s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] . weights=distance, n_neighbors=10, score=0.453060, total=   0.4s
[CV] .. weights=uniform, n_neighbors=11, score=0.379211, total=   0.3s
[CV] .. weights=uniform, n_neighbors=11, score=0.331915, total=   0.2s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] .. weights=uniform, n_neighbors=11, score=0.422248, total=   0.3s
[CV] .. weights=uniform, n_neighbors=11, score=0.412254, total=   0.3s
[CV] weights=uniform, n_neighbors=11 .................................
[CV] weights=uniform, n_neighbors=11 .................................
[CV] .. weights=uniform, n_neighbors=11, score=0.456793, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.415746, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.381440, total=   0.2s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.370103, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.410919, total=   0.4s
[CV] .. weights=uniform, n_neighbors=11, score=0.479606, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.331927, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.416460, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.366435, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.424703, total=   0.3s
[CV] .. weights=uniform, n_neighbors=11, score=0.361822, total=   0.5s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.422919, total=   0.5s
[CV] weights=distance, n_neighbors=11 ................................
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.493364, total=   0.2s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.473447, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.437099, total=   0.2s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.488575, total=   0.2s
[CV] .. weights=uniform, n_neighbors=11, score=0.458700, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.426746, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.453714, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.406634, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.449907, total=   0.4s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.465270, total=   0.2s
[CV] weights=distance, n_neighbors=11 ................................
[CV] . weights=distance, n_neighbors=11, score=0.432213, total=   0.4s
[CV] .. weights=uniform, n_neighbors=11, score=0.419815, total=   0.5s
[CV] . weights=distance, n_neighbors=11, score=0.384087, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] weights=distance, n_neighbors=11 ................................
[CV] weights=distance, n_neighbors=11 ................................
[CV] . weights=distance, n_neighbors=11, score=0.421622, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] . weights=distance, n_neighbors=11, score=0.458262, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] .. weights=uniform, n_neighbors=11, score=0.517498, total=   0.3s
[CV] weights=distance, n_neighbors=11 ................................
[CV] . weights=distance, n_neighbors=11, score=0.339574, total=   0.2s
[CV] . weights=distance, n_neighbors=11, score=0.360574, total=   0.3s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.410085, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.416647, total=   0.4s
[CV] . weights=distance, n_neighbors=11, score=0.383910, total=   0.2s
[CV] . weights=distance, n_neighbors=11, score=0.483043, total=   0.3s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.333404, total=   0.4s
[CV] . weights=distance, n_neighbors=11, score=0.364631, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.414398, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.496777, total=   0.2s
[CV] . weights=distance, n_neighbors=11, score=0.422501, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.362749, total=   0.3s
[CV] . weights=distance, n_neighbors=11, score=0.422232, total=   0.4s
[CV] . weights=distance, n_neighbors=11, score=0.471512, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.428800, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.469150, total=   0.2s
[CV] . weights=distance, n_neighbors=11, score=0.448052, total=   0.3s
[CV] . weights=distance, n_neighbors=11, score=0.409271, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.443521, total=   0.3s
[CV] . weights=distance, n_neighbors=11, score=0.454409, total=   0.3s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.421624, total=   0.3s
[CV] . weights=distance, n_neighbors=11, score=0.520849, total=   0.2s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.493145, total=   0.2s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] .. weights=uniform, n_neighbors=12, score=0.411792, total=   0.3s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] .. weights=uniform, n_neighbors=12, score=0.386525, total=   0.4s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] .. weights=uniform, n_neighbors=12, score=0.419467, total=   0.3s
[CV] .. weights=uniform, n_neighbors=12, score=0.333191, total=   0.2s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] weights=uniform, n_neighbors=12 .................................
[CV] . weights=distance, n_neighbors=11, score=0.451971, total=   0.5s
[CV] weights=uniform, n_neighbors=12 .................................
[CV] .. weights=uniform, n_neighbors=12, score=0.457038, total=   0.4s
[CV] .. weights=uniform, n_neighbors=12, score=0.415746, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.414461, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.478460, total=   0.4s
[CV] .. weights=uniform, n_neighbors=12, score=0.367852, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.390614, total=   0.2s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.374918, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.366219, total=   0.5s
[CV] .. weights=uniform, n_neighbors=12, score=0.335725, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.420960, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.490709, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.422649, total=   0.4s
[CV] .. weights=uniform, n_neighbors=12, score=0.422919, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.438232, total=   0.2s
[CV] .. weights=uniform, n_neighbors=12, score=0.469146, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.429941, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.450603, total=   0.3s
[CV] .. weights=uniform, n_neighbors=12, score=0.409051, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.455267, total=   0.4s
[CV] .. weights=uniform, n_neighbors=12, score=0.486913, total=   0.2s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.421624, total=   0.3s
[CV] .. weights=uniform, n_neighbors=12, score=0.515264, total=   0.2s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] .. weights=uniform, n_neighbors=12, score=0.464106, total=   0.2s
[CV] .. weights=uniform, n_neighbors=12, score=0.448704, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] weights=distance, n_neighbors=12 ................................
[CV] . weights=distance, n_neighbors=12, score=0.430591, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] . weights=distance, n_neighbors=12, score=0.384530, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] . weights=distance, n_neighbors=12, score=0.422797, total=   0.3s
[CV] weights=distance, n_neighbors=12 ................................
[CV] . weights=distance, n_neighbors=12, score=0.417341, total=   0.4s
[CV] weights=distance, n_neighbors=12 ................................
[CV] . weights=distance, n_neighbors=12, score=0.464137, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.337872, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.483731, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.367520, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.409252, total=   0.4s
[CV] . weights=distance, n_neighbors=12, score=0.368133, total=   0.4s
[CV] . weights=distance, n_neighbors=12, score=0.412954, total=   0.3s
[CV] . weights=distance, n_neighbors=12, score=0.426807, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.470436, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.497914, total=   0.2s
[CV] . weights=distance, n_neighbors=12, score=0.360574, total=   0.5s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.333615, total=   0.4s
[CV] . weights=distance, n_neighbors=12, score=0.427430, total=   0.4s
[CV] . weights=distance, n_neighbors=12, score=0.440876, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.382851, total=   0.3s
[CV] . weights=distance, n_neighbors=12, score=0.424923, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.411907, total=   0.3s
[CV] . weights=distance, n_neighbors=12, score=0.471090, total=   0.2s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.493976, total=   0.2s
[CV] . weights=distance, n_neighbors=12, score=0.451531, total=   0.5s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.423886, total=   0.4s
[CV] . weights=distance, n_neighbors=12, score=0.453551, total=   0.4s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] . weights=distance, n_neighbors=12, score=0.519360, total=   0.2s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] .. weights=uniform, n_neighbors=13, score=0.379211, total=   0.3s
[CV] . weights=distance, n_neighbors=12, score=0.451971, total=   0.4s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] .. weights=uniform, n_neighbors=13, score=0.424334, total=   0.3s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] .. weights=uniform, n_neighbors=13, score=0.420447, total=   0.3s
[CV] .. weights=uniform, n_neighbors=13, score=0.333191, total=   0.2s
[CV] weights=uniform, n_neighbors=13 .................................
[CV] weights=uniform, n_neighbors=13 .................................
[CV] .. weights=uniform, n_neighbors=13, score=0.360574, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.409669, total=   0.3s
[CV] .. weights=uniform, n_neighbors=13, score=0.458262, total=   0.4s
[CV] .. weights=uniform, n_neighbors=13, score=0.412486, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.371639, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.412748, total=   0.3s
[CV] .. weights=uniform, n_neighbors=13, score=0.480293, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.335936, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.378264, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.367695, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.495260, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.424291, total=   0.4s
[CV] .. weights=uniform, n_neighbors=13, score=0.468286, total=   0.5s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.430625, total=   0.5s
[CV] .. weights=uniform, n_neighbors=13, score=0.457842, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.438610, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.410149, total=   0.4s
[CV] .. weights=uniform, n_neighbors=13, score=0.426244, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.487329, total=   0.2s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.455937, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.422981, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.467598, total=   0.2s
[CV] .. weights=uniform, n_neighbors=13, score=0.452843, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] .. weights=uniform, n_neighbors=13, score=0.520849, total=   0.2s
[CV] . weights=distance, n_neighbors=13, score=0.415491, total=   0.3s
[CV] weights=distance, n_neighbors=13 ................................
[CV] . weights=distance, n_neighbors=13, score=0.462179, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] . weights=distance, n_neighbors=13, score=0.338723, total=   0.2s
[CV] weights=distance, n_neighbors=13 ................................
[CV] . weights=distance, n_neighbors=13, score=0.431750, total=   0.4s
[CV] weights=distance, n_neighbors=13 ................................
[CV] weights=distance, n_neighbors=13 ................................
[CV] . weights=distance, n_neighbors=13, score=0.427027, total=   0.4s
[CV] . weights=distance, n_neighbors=13, score=0.364193, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.482126, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.412794, total=   0.4s
[CV] . weights=distance, n_neighbors=13, score=0.383644, total=   0.5s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.357455, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.378970, total=   0.3s
[CV] . weights=distance, n_neighbors=13, score=0.409035, total=   0.4s
[CV] . weights=distance, n_neighbors=13, score=0.366652, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.468286, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.499810, total=   0.2s
[CV] . weights=distance, n_neighbors=13, score=0.427722, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.440121, total=   0.2s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.337413, total=   0.5s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.428446, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.411028, total=   0.3s
[CV] . weights=distance, n_neighbors=13, score=0.431538, total=   0.5s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.451971, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.453551, total=   0.3s
[CV] . weights=distance, n_neighbors=13, score=0.424112, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.492730, total=   0.2s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.521966, total=   0.2s
[CV] .. weights=uniform, n_neighbors=14, score=0.421784, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] .. weights=uniform, n_neighbors=14, score=0.415029, total=   0.3s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] . weights=distance, n_neighbors=13, score=0.475359, total=   0.3s
[CV] . weights=distance, n_neighbors=13, score=0.457560, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] .. weights=uniform, n_neighbors=14, score=0.386968, total=   0.4s
[CV] weights=uniform, n_neighbors=14 .................................
[CV] .. weights=uniform, n_neighbors=14, score=0.478460, total=   0.3s
[CV] .. weights=uniform, n_neighbors=14, score=0.338298, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=uniform, n_neighbors=14 .................................
[CV] .. weights=uniform, n_neighbors=14, score=0.452142, total=   0.5s
[CV] .. weights=uniform, n_neighbors=14, score=0.422797, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.372948, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.412586, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.371422, total=   0.4s
[CV] .. weights=uniform, n_neighbors=14, score=0.366812, total=   0.5s
[CV] .. weights=uniform, n_neighbors=14, score=0.422461, total=   0.3s
[CV] .. weights=uniform, n_neighbors=14, score=0.339312, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.488434, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.384263, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.423162, total=   0.5s
[CV] .. weights=uniform, n_neighbors=14, score=0.436721, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.432451, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.418936, total=   0.5s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.409930, total=   0.3s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.457328, total=   0.4s
[CV] .. weights=uniform, n_neighbors=14, score=0.458914, total=   0.3s
[CV] .. weights=uniform, n_neighbors=14, score=0.465061, total=   0.5s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.467598, total=   0.2s
[CV] .. weights=uniform, n_neighbors=14, score=0.482759, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.515637, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] . weights=distance, n_neighbors=14, score=0.415954, total=   0.3s
[CV] weights=distance, n_neighbors=14 ................................






    



[Parallel(n_jobs=-1)]: Done 752 tasks      | elapsed:   21.2s






    



[CV] .. weights=uniform, n_neighbors=14, score=0.451318, total=   0.4s
[CV] . weights=distance, n_neighbors=14, score=0.432677, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] . weights=distance, n_neighbors=14, score=0.387190, total=   0.4s
[CV] . weights=distance, n_neighbors=14, score=0.342553, total=   0.2s
[CV] weights=distance, n_neighbors=14 ................................
[CV] .. weights=uniform, n_neighbors=14, score=0.422303, total=   0.4s
[CV] weights=distance, n_neighbors=14 ................................
[CV] weights=distance, n_neighbors=14 ................................
[CV] . weights=distance, n_neighbors=14, score=0.463892, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.485564, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.358910, total=   0.4s
[CV] . weights=distance, n_neighbors=14, score=0.370978, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.381087, total=   0.2s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.429377, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.409460, total=   0.5s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.339101, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.369905, total=   0.4s
[CV] . weights=distance, n_neighbors=14, score=0.427264, total=   0.3s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.411097, total=   0.5s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.466566, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.432451, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.426464, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.411687, total=   0.4s
[CV] . weights=distance, n_neighbors=14, score=0.498673, total=   0.3s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.442388, total=   0.3s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.424112, total=   0.3s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.460807, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.519360, total=   0.2s
[CV] . weights=distance, n_neighbors=14, score=0.494391, total=   0.3s
[CV] . weights=distance, n_neighbors=14, score=0.455239, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.474971, total=   0.3s
[CV] .. weights=uniform, n_neighbors=15, score=0.424565, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] . weights=distance, n_neighbors=14, score=0.456769, total=   0.5s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] .. weights=uniform, n_neighbors=15, score=0.414566, total=   0.3s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=uniform, n_neighbors=15 .................................
[CV] .. weights=uniform, n_neighbors=15, score=0.454345, total=   0.3s
[CV] .. weights=uniform, n_neighbors=15, score=0.386303, total=   0.4s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] .. weights=uniform, n_neighbors=15, score=0.339149, total=   0.2s
[CV] weights=uniform, n_neighbors=15 .................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.423972, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.480752, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.370541, total=   0.4s
[CV] .. weights=uniform, n_neighbors=15, score=0.382498, total=   0.2s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.414045, total=   0.6s
[CV] .. weights=uniform, n_neighbors=15, score=0.375325, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.364109, total=   0.5s
[CV] .. weights=uniform, n_neighbors=15, score=0.422004, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.411716, total=   0.5s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.495260, total=   0.3s
[CV] .. weights=uniform, n_neighbors=15, score=0.467641, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.334037, total=   0.5s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.431310, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.439365, total=   0.2s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.423822, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.414982, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.454409, total=   0.4s
[CV] .. weights=uniform, n_neighbors=15, score=0.481928, total=   0.2s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.420267, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] . weights=distance, n_neighbors=15, score=0.431518, total=   0.3s
[CV] .. weights=uniform, n_neighbors=15, score=0.469538, total=   0.2s
[CV] weights=distance, n_neighbors=15 ................................
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.517498, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.454149, total=   0.3s
[CV] weights=distance, n_neighbors=15 ................................
[CV] .. weights=uniform, n_neighbors=15, score=0.455937, total=   0.5s
[CV] weights=distance, n_neighbors=15 ................................
[CV] . weights=distance, n_neighbors=15, score=0.387190, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] . weights=distance, n_neighbors=15, score=0.418497, total=   0.4s
[CV] weights=distance, n_neighbors=15 ................................
[CV] . weights=distance, n_neighbors=15, score=0.341277, total=   0.2s
[CV] weights=distance, n_neighbors=15 ................................
[CV] . weights=distance, n_neighbors=15, score=0.411961, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.484647, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.430082, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.460710, total=   0.4s
[CV] . weights=distance, n_neighbors=15, score=0.358910, total=   0.5s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.372073, total=   0.4s
[CV] . weights=distance, n_neighbors=15, score=0.408416, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.366601, total=   0.4s
[CV] . weights=distance, n_neighbors=15, score=0.380381, total=   0.3s
[CV] . weights=distance, n_neighbors=15, score=0.334881, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.413225, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.466996, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.454409, total=   0.4s
[CV] . weights=distance, n_neighbors=15, score=0.500948, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.493560, total=   0.2s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.425363, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.423434, total=   0.3s
[CV] . weights=distance, n_neighbors=15, score=0.427493, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.443899, total=   0.2s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.434961, total=   0.5s
[CV] . weights=distance, n_neighbors=15, score=0.460575, total=   0.5s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.475747, total=   0.2s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] .. weights=uniform, n_neighbors=16, score=0.388520, total=   0.3s
[CV] . weights=distance, n_neighbors=15, score=0.522710, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] weights=uniform, n_neighbors=16 .................................
[CV] . weights=distance, n_neighbors=15, score=0.453496, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] .. weights=uniform, n_neighbors=16, score=0.424565, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] .. weights=uniform, n_neighbors=16, score=0.413179, total=   0.4s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] .. weights=uniform, n_neighbors=16, score=0.368060, total=   0.3s
[CV] weights=uniform, n_neighbors=16 .................................
[CV] .. weights=uniform, n_neighbors=16, score=0.424677, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.448960, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.339574, total=   0.2s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.373386, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.414045, total=   0.4s
[CV] .. weights=uniform, n_neighbors=16, score=0.413985, total=   0.4s
[CV] .. weights=uniform, n_neighbors=16, score=0.342055, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.475940, total=   0.5s
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.387438, total=   0.2s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.491468, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.463771, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.437476, total=   0.2s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.433136, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.372290, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.424703, total=   0.5s
[CV] .. weights=uniform, n_neighbors=16, score=0.451405, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.421546, total=   0.5s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.412566, total=   0.5s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.482759, total=   0.2s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.471867, total=   0.2s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.420719, total=   0.4s
[CV] .. weights=uniform, n_neighbors=16, score=0.463822, total=   0.4s
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=distance, n_neighbors=16 ................................
[CV] . weights=distance, n_neighbors=16, score=0.434762, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] . weights=distance, n_neighbors=16, score=0.416416, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] . weights=distance, n_neighbors=16, score=0.462668, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] .. weights=uniform, n_neighbors=16, score=0.512658, total=   0.2s
[CV] .. weights=uniform, n_neighbors=16, score=0.449575, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] . weights=distance, n_neighbors=16, score=0.389184, total=   0.5s
[CV] . weights=distance, n_neighbors=16, score=0.432902, total=   0.3s
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=distance, n_neighbors=16 ................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.485793, total=   0.4s
[CV] . weights=distance, n_neighbors=16, score=0.362030, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.345532, total=   0.2s
[CV] . weights=distance, n_neighbors=16, score=0.370541, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.383910, total=   0.2s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.375325, total=   0.4s
[CV] . weights=distance, n_neighbors=16, score=0.411127, total=   0.5s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.340367, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.426121, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.408828, total=   0.6s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.432679, total=   0.3s
[CV] . weights=distance, n_neighbors=16, score=0.499052, total=   0.3s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.440876, total=   0.2s
[CV] . weights=distance, n_neighbors=16, score=0.425804, total=   0.5s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.465921, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.464054, total=   0.3s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.452263, total=   0.5s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.411907, total=   0.4s
[CV] . weights=distance, n_neighbors=16, score=0.478851, total=   0.2s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.451971, total=   0.3s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] .. weights=uniform, n_neighbors=17, score=0.425956, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.420719, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] .. weights=uniform, n_neighbors=17, score=0.414566, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] .. weights=uniform, n_neighbors=17, score=0.340851, total=   0.2s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.491483, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] . weights=distance, n_neighbors=16, score=0.520849, total=   0.2s
[CV] .. weights=uniform, n_neighbors=17, score=0.456548, total=   0.4s
[CV] weights=uniform, n_neighbors=17 .................................
[CV] weights=uniform, n_neighbors=17 .................................
[CV] .. weights=uniform, n_neighbors=17, score=0.385638, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.415712, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.477773, total=   0.3s
[CV] .. weights=uniform, n_neighbors=17, score=0.427967, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.368352, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.365773, total=   0.6s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.425663, total=   0.5s
[CV] .. weights=uniform, n_neighbors=17, score=0.411097, total=   0.4s
[CV] .. weights=uniform, n_neighbors=17, score=0.341211, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.430647, total=   0.3s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.468071, total=   0.4s
[CV] .. weights=uniform, n_neighbors=17, score=0.416081, total=   0.4s
[CV] .. weights=uniform, n_neighbors=17, score=0.499052, total=   0.3s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.381440, total=   0.3s
[CV] .. weights=uniform, n_neighbors=17, score=0.376193, total=   0.5s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.437099, total=   0.3s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.483174, total=   0.2s
[CV] .. weights=uniform, n_neighbors=17, score=0.463822, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.452049, total=   0.5s
[CV] .. weights=uniform, n_neighbors=17, score=0.430397, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.471867, total=   0.3s
[CV] .. weights=uniform, n_neighbors=17, score=0.453060, total=   0.3s
[CV] . weights=distance, n_neighbors=17, score=0.388298, total=   0.3s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.422981, total=   0.5s
[CV] weights=distance, n_neighbors=17 ................................
[CV] . weights=distance, n_neighbors=17, score=0.417572, total=   0.3s
[CV] weights=distance, n_neighbors=17 ................................
[CV] .. weights=uniform, n_neighbors=17, score=0.514892, total=   0.3s
[CV] . weights=distance, n_neighbors=17, score=0.433835, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] weights=distance, n_neighbors=17 ................................
[CV] . weights=distance, n_neighbors=17, score=0.460465, total=   0.4s
[CV] weights=distance, n_neighbors=17 ................................
[CV] . weights=distance, n_neighbors=17, score=0.430082, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.343830, total=   0.3s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.484418, total=   0.3s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.410294, total=   0.5s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.340156, total=   0.4s
[CV] . weights=distance, n_neighbors=17, score=0.365288, total=   0.5s
[CV] . weights=distance, n_neighbors=17, score=0.360782, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.500569, total=   0.2s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.405116, total=   0.3s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.425363, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.379323, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.424977, total=   0.5s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.439365, total=   0.2s
[CV] . weights=distance, n_neighbors=17, score=0.375976, total=   0.4s
[CV] . weights=distance, n_neighbors=17, score=0.465921, total=   0.5s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.433136, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.489406, total=   0.3s
[CV] . weights=distance, n_neighbors=17, score=0.450976, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.411467, total=   0.4s
[CV] . weights=distance, n_neighbors=17, score=0.461735, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.418910, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] .. weights=uniform, n_neighbors=18, score=0.422016, total=   0.3s
[CV] . weights=distance, n_neighbors=17, score=0.450447, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.517870, total=   0.3s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] .. weights=uniform, n_neighbors=18, score=0.386082, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] .. weights=uniform, n_neighbors=18, score=0.415723, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] .. weights=uniform, n_neighbors=18, score=0.449939, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] . weights=distance, n_neighbors=17, score=0.473031, total=   0.4s
[CV] weights=uniform, n_neighbors=18 .................................
[CV] .. weights=uniform, n_neighbors=18, score=0.338298, total=   0.2s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.469523, total=   0.4s
[CV] .. weights=uniform, n_neighbors=18, score=0.424677, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.366188, total=   0.5s
[CV] .. weights=uniform, n_neighbors=18, score=0.345854, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.374241, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.422919, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.417170, total=   0.5s
[CV] .. weights=uniform, n_neighbors=18, score=0.499052, total=   0.2s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.459901, total=   0.4s
[CV] .. weights=uniform, n_neighbors=18, score=0.384968, total=   0.3s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.372510, total=   0.4s
[CV] .. weights=uniform, n_neighbors=18, score=0.408622, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.429326, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.440121, total=   0.3s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.434277, total=   0.5s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.483590, total=   0.2s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.418058, total=   0.5s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.420719, total=   0.4s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.464518, total=   0.4s
[CV] .. weights=uniform, n_neighbors=18, score=0.449045, total=   0.5s
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.469926, total=   0.2s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] .. weights=uniform, n_neighbors=18, score=0.506329, total=   0.3s
[CV] weights=distance, n_neighbors=18 ................................
[CV] . weights=distance, n_neighbors=18, score=0.434994, total=   0.3s
[CV] . weights=distance, n_neighbors=18, score=0.432902, total=   0.3s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] . weights=distance, n_neighbors=18, score=0.459731, total=   0.3s
[CV] . weights=distance, n_neighbors=18, score=0.418266, total=   0.3s
[CV] .. weights=uniform, n_neighbors=18, score=0.447615, total=   0.5s
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] weights=distance, n_neighbors=18 ................................
[CV] . weights=distance, n_neighbors=18, score=0.388741, total=   0.4s
[CV] . weights=distance, n_neighbors=18, score=0.345106, total=   0.2s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.483501, total=   0.4s
[CV] . weights=distance, n_neighbors=18, score=0.381440, total=   0.2s
[CV] . weights=distance, n_neighbors=18, score=0.410919, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.341000, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.378144, total=   0.4s
[CV] . weights=distance, n_neighbors=18, score=0.369446, total=   0.5s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.406353, total=   0.5s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.425435, total=   0.5s
[CV] . weights=distance, n_neighbors=18, score=0.361822, total=   0.6s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.430647, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.464631, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.413884, total=   0.4s
[CV] . weights=distance, n_neighbors=18, score=0.502844, total=   0.3s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.442388, total=   0.3s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.488575, total=   0.2s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.449045, total=   0.5s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.432679, total=   0.6s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.420719, total=   0.4s
[CV] . weights=distance, n_neighbors=18, score=0.462894, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.517870, total=   0.2s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.451536, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] . weights=distance, n_neighbors=18, score=0.473807, total=   0.3s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] .. weights=uniform, n_neighbors=19, score=0.425724, total=   0.4s
[CV] .. weights=uniform, n_neighbors=19, score=0.416416, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] .. weights=uniform, n_neighbors=19, score=0.449449, total=   0.3s
[CV] .. weights=uniform, n_neighbors=19, score=0.382757, total=   0.4s
[CV] weights=uniform, n_neighbors=19 .................................
[CV] .. weights=uniform, n_neighbors=19, score=0.469753, total=   0.3s
[CV] .. weights=uniform, n_neighbors=19, score=0.416337, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=uniform, n_neighbors=19 .................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.366396, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.340000, total=   0.3s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.428672, total=   0.5s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.368571, total=   0.5s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.381440, total=   0.2s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.409035, total=   0.4s
[CV] .. weights=uniform, n_neighbors=19, score=0.422690, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.504361, total=   0.2s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.434830, total=   0.4s
[CV] .. weights=uniform, n_neighbors=19, score=0.342899, total=   0.5s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.463341, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.374675, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.423111, total=   0.4s
[CV] .. weights=uniform, n_neighbors=19, score=0.451834, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.462199, total=   0.4s
[CV] .. weights=uniform, n_neighbors=19, score=0.434733, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.481097, total=   0.2s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.472643, total=   0.2s
[CV] .. weights=uniform, n_neighbors=19, score=0.445032, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] . weights=distance, n_neighbors=19, score=0.418035, total=   0.3s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] . weights=distance, n_neighbors=19, score=0.433140, total=   0.4s
[CV] . weights=distance, n_neighbors=19, score=0.457283, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.511914, total=   0.2s
[CV] weights=distance, n_neighbors=19 ................................
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.423207, total=   0.5s
[CV] weights=distance, n_neighbors=19 ................................
[CV] .. weights=uniform, n_neighbors=19, score=0.453714, total=   0.5s
[CV] weights=distance, n_neighbors=19 ................................
[CV] . weights=distance, n_neighbors=19, score=0.387633, total=   0.4s
[CV] weights=distance, n_neighbors=19 ................................
[CV] . weights=distance, n_neighbors=19, score=0.344255, total=   0.2s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.431257, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.343110, total=   0.4s
[CV] . weights=distance, n_neighbors=19, score=0.361614, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.383204, total=   0.3s
[CV] . weights=distance, n_neighbors=19, score=0.414045, total=   0.5s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.481439, total=   0.6s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.407591, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.367695, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.504740, total=   0.2s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.378144, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.463986, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.423376, total=   0.6s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.417399, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.434277, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.433289, total=   0.5s
[CV] . weights=distance, n_neighbors=19, score=0.441254, total=   0.2s
[CV] . weights=distance, n_neighbors=19, score=0.451405, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.472643, total=   0.2s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.487329, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.518615, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.423207, total=   0.4s
[CV] . weights=distance, n_neighbors=19, score=0.463590, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=uniform, n_neighbors=20 .................................
[CV] . weights=distance, n_neighbors=19, score=0.454585, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] .. weights=uniform, n_neighbors=20, score=0.428505, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] .. weights=uniform, n_neighbors=20, score=0.447736, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] .. weights=uniform, n_neighbors=20, score=0.386525, total=   0.3s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] .. weights=uniform, n_neighbors=20, score=0.428672, total=   0.3s
[CV] .. weights=uniform, n_neighbors=20, score=0.339574, total=   0.2s
[CV] .. weights=uniform, n_neighbors=20, score=0.467003, total=   0.4s
[CV] weights=uniform, n_neighbors=20 .................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.370759, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.420809, total=   0.5s
[CV] .. weights=uniform, n_neighbors=20, score=0.425922, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.347964, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.412335, total=   0.3s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.465706, total=   0.4s
[CV] .. weights=uniform, n_neighbors=20, score=0.367852, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.508153, total=   0.3s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.376626, total=   0.4s
[CV] .. weights=uniform, n_neighbors=20, score=0.424977, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.422452, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.479020, total=   0.2s
[CV] .. weights=uniform, n_neighbors=20, score=0.387085, total=   0.3s
[CV] .. weights=uniform, n_neighbors=20, score=0.437015, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.446543, total=   0.3s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.461039, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.435711, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.474971, total=   0.2s
[CV] weights=distance, n_neighbors=20 ................................
[CV] . weights=distance, n_neighbors=20, score=0.440556, total=   0.4s
[CV] .. weights=uniform, n_neighbors=20, score=0.508935, total=   0.2s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] . weights=distance, n_neighbors=20, score=0.389849, total=   0.4s
[CV] .. weights=uniform, n_neighbors=20, score=0.449474, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.421398, total=   0.5s
[CV] weights=distance, n_neighbors=20 ................................
[CV] . weights=distance, n_neighbors=20, score=0.422197, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] .. weights=uniform, n_neighbors=20, score=0.454803, total=   0.4s
[CV] . weights=distance, n_neighbors=20, score=0.460220, total=   0.3s
[CV] weights=distance, n_neighbors=20 ................................
[CV] . weights=distance, n_neighbors=20, score=0.347660, total=   0.4s
[CV] weights=distance, n_neighbors=20 ................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.415712, total=   0.4s
[CV] . weights=distance, n_neighbors=20, score=0.433373, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.481668, total=   0.5s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.425435, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.344587, total=   0.4s
[CV] . weights=distance, n_neighbors=20, score=0.377493, total=   0.5s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.362445, total=   0.5s
[CV] . weights=distance, n_neighbors=20, score=0.408210, total=   0.4s
[CV] . weights=distance, n_neighbors=20, score=0.504361, total=   0.2s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.432849, total=   0.4s
[CV] . weights=distance, n_neighbors=20, score=0.369009, total=   0.5s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.383910, total=   0.3s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.437700, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................






    



[Parallel(n_jobs=-1)]: Done 1104 tasks      | elapsed:   36.9s






    



[CV] . weights=distance, n_neighbors=20, score=0.465921, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.489406, total=   0.3s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.449903, total=   0.5s
[CV] . weights=distance, n_neighbors=20, score=0.424791, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.465677, total=   0.5s
[CV] . weights=distance, n_neighbors=20, score=0.417179, total=   0.5s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.476523, total=   0.2s
[CV] . weights=distance, n_neighbors=20, score=0.521593, total=   0.2s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] .. weights=uniform, n_neighbors=21, score=0.431750, total=   0.3s
[CV] . weights=distance, n_neighbors=20, score=0.446543, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] .. weights=uniform, n_neighbors=21, score=0.453121, total=   0.3s
[CV] .. weights=uniform, n_neighbors=21, score=0.383200, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=uniform, n_neighbors=21 .................................
[CV] .. weights=uniform, n_neighbors=21, score=0.430317, total=   0.3s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] . weights=distance, n_neighbors=20, score=0.457635, total=   0.4s
[CV] .. weights=uniform, n_neighbors=21, score=0.420809, total=   0.4s
[CV] weights=uniform, n_neighbors=21 .................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.340851, total=   0.2s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.372510, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.345854, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.379662, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.469982, total=   0.5s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.384263, total=   0.3s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.411922, total=   0.4s
[CV] .. weights=uniform, n_neighbors=21, score=0.425435, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.425922, total=   0.7s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.510808, total=   0.2s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.367644, total=   0.7s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.436372, total=   0.5s
[CV] .. weights=uniform, n_neighbors=21, score=0.445788, total=   0.2s
[CV] weights=distance, n_neighbors=21 ................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.424429, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.469146, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.435874, total=   0.5s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.481097, total=   0.3s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.464981, total=   0.5s
[CV] .. weights=uniform, n_neighbors=21, score=0.452478, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.429315, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.458506, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] .. weights=uniform, n_neighbors=21, score=0.475359, total=   0.3s
[CV] . weights=distance, n_neighbors=21, score=0.386303, total=   0.5s
[CV] .. weights=uniform, n_neighbors=21, score=0.511541, total=   0.3s
[CV] weights=distance, n_neighbors=21 ................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] weights=distance, n_neighbors=21 ................................
[CV] . weights=distance, n_neighbors=21, score=0.437775, total=   0.5s
[CV] weights=distance, n_neighbors=21 ................................
[CV] . weights=distance, n_neighbors=21, score=0.422197, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] . weights=distance, n_neighbors=21, score=0.460465, total=   0.4s
[CV] weights=distance, n_neighbors=21 ................................
[CV] . weights=distance, n_neighbors=21, score=0.432197, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.477773, total=   0.3s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.343830, total=   0.3s
[CV] . weights=distance, n_neighbors=21, score=0.362030, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.407384, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.383204, total=   0.3s
[CV] . weights=distance, n_neighbors=21, score=0.343110, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.370103, total=   0.6s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.418629, total=   0.6s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.381830, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.424520, total=   0.5s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.433950, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.437471, total=   0.4s
[CV] . weights=distance, n_neighbors=21, score=0.419815, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.466781, total=   0.5s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.508153, total=   0.3s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.474583, total=   0.3s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.518987, total=   0.2s
[CV] . weights=distance, n_neighbors=21, score=0.467069, total=   0.4s
[CV] . weights=distance, n_neighbors=21, score=0.446543, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.427505, total=   0.4s
[CV] . weights=distance, n_neighbors=21, score=0.451191, total=   0.5s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.489406, total=   0.3s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] . weights=distance, n_neighbors=21, score=0.457852, total=   0.5s
[CV] .. weights=uniform, n_neighbors=22, score=0.385860, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] .. weights=uniform, n_neighbors=22, score=0.450184, total=   0.4s
[CV] .. weights=uniform, n_neighbors=22, score=0.340000, total=   0.2s
[CV] .. weights=uniform, n_neighbors=22, score=0.431054, total=   0.6s
[CV] .. weights=uniform, n_neighbors=22, score=0.422890, total=   0.4s
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=uniform, n_neighbors=22 .................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.430787, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.425714, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.372292, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.469982, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.385674, total=   0.3s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.346487, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.369723, total=   0.6s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.377493, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.413573, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.422919, total=   0.5s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.470651, total=   0.5s
[CV] .. weights=uniform, n_neighbors=22, score=0.439297, total=   0.4s
[CV] .. weights=uniform, n_neighbors=22, score=0.508912, total=   0.3s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.435931, total=   0.5s
[CV] weights=distance, n_neighbors=22 ................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.446921, total=   0.3s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.425747, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.452263, total=   0.5s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.481928, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.467764, total=   0.5s
[CV] . weights=distance, n_neighbors=22, score=0.439166, total=   0.4s
[CV] .. weights=uniform, n_neighbors=22, score=0.428184, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.459159, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.510052, total=   0.3s
[CV] weights=distance, n_neighbors=22 ................................
[CV] . weights=distance, n_neighbors=22, score=0.425434, total=   0.4s
[CV] . weights=distance, n_neighbors=22, score=0.386746, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] weights=distance, n_neighbors=22 ................................
[CV] . weights=distance, n_neighbors=22, score=0.433137, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] . weights=distance, n_neighbors=22, score=0.461934, total=   0.4s
[CV] weights=distance, n_neighbors=22 ................................
[CV] .. weights=uniform, n_neighbors=22, score=0.476523, total=   0.2s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.419671, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.480522, total=   0.3s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.369009, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.364733, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.382480, total=   0.5s
[CV] . weights=distance, n_neighbors=22, score=0.345532, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.345643, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.410479, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.426121, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.509670, total=   0.3s
[CV] . weights=distance, n_neighbors=22, score=0.434610, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.440895, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.424649, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.451454, total=   0.3s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.467211, total=   0.6s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.469620, total=   0.5s
[CV] . weights=distance, n_neighbors=22, score=0.454838, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.384263, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.478851, total=   0.3s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.461991, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.488160, total=   0.3s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] . weights=distance, n_neighbors=22, score=0.522338, total=   0.2s
[CV] . weights=distance, n_neighbors=22, score=0.428636, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] weights=uniform, n_neighbors=23 .................................
[CV] .. weights=uniform, n_neighbors=23, score=0.431750, total=   0.4s
[CV] .. weights=uniform, n_neighbors=23, score=0.382757, total=   0.5s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] weights=uniform, n_neighbors=23 .................................
[CV] .. weights=uniform, n_neighbors=23, score=0.419653, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] .. weights=uniform, n_neighbors=23, score=0.451897, total=   0.4s
[CV] weights=uniform, n_neighbors=23 .................................
[CV] .. weights=uniform, n_neighbors=23, score=0.427797, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.333617, total=   0.4s
[CV] .. weights=uniform, n_neighbors=23, score=0.434078, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.467919, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.410479, total=   0.4s
[CV] .. weights=uniform, n_neighbors=23, score=0.372948, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.370347, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.384615, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.424748, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.427944, total=   0.3s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.511945, total=   0.3s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.379011, total=   0.6s
[CV] .. weights=uniform, n_neighbors=23, score=0.435711, total=   0.4s
[CV] .. weights=uniform, n_neighbors=23, score=0.468071, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.343743, total=   0.6s
[CV] .. weights=uniform, n_neighbors=23, score=0.447677, total=   0.3s
[CV] .. weights=uniform, n_neighbors=23, score=0.439982, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.482732, total=   0.2s
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.484420, total=   0.2s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.453551, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.473794, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.431350, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] .. weights=uniform, n_neighbors=23, score=0.461773, total=   0.4s
[CV] .. weights=uniform, n_neighbors=23, score=0.513031, total=   0.3s
[CV] weights=distance, n_neighbors=23 ................................
[CV] weights=distance, n_neighbors=23 ................................
[CV] . weights=distance, n_neighbors=23, score=0.423815, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] . weights=distance, n_neighbors=23, score=0.385860, total=   0.5s
[CV] weights=distance, n_neighbors=23 ................................
[CV] . weights=distance, n_neighbors=23, score=0.442874, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] . weights=distance, n_neighbors=23, score=0.461689, total=   0.4s
[CV] weights=distance, n_neighbors=23 ................................
[CV] . weights=distance, n_neighbors=23, score=0.434078, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.340426, total=   0.2s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.345432, total=   0.4s
[CV] . weights=distance, n_neighbors=23, score=0.366188, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.383565, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.387085, total=   0.3s
[CV] . weights=distance, n_neighbors=23, score=0.475023, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.425505, total=   0.6s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.409035, total=   0.5s
[CV] . weights=distance, n_neighbors=23, score=0.515358, total=   0.2s
[CV] . weights=distance, n_neighbors=23, score=0.439894, total=   0.4s
[CV] . weights=distance, n_neighbors=23, score=0.371635, total=   0.4s
[CV] . weights=distance, n_neighbors=23, score=0.443633, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.426807, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.424868, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.469791, total=   0.6s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.450321, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.491068, total=   0.3s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.455482, total=   0.7s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.462644, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.474026, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.484284, total=   0.3s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] . weights=distance, n_neighbors=23, score=0.519360, total=   0.3s
[CV] . weights=distance, n_neighbors=23, score=0.428636, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] weights=uniform, n_neighbors=24 .................................
[CV] .. weights=uniform, n_neighbors=24, score=0.433140, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] .. weights=uniform, n_neighbors=24, score=0.382314, total=   0.4s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] .. weights=uniform, n_neighbors=24, score=0.452387, total=   0.3s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] .. weights=uniform, n_neighbors=24, score=0.424748, total=   0.4s
[CV] .. weights=uniform, n_neighbors=24, score=0.469523, total=   0.5s
[CV] weights=uniform, n_neighbors=24 .................................
[CV] .. weights=uniform, n_neighbors=24, score=0.376843, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.340851, total=   0.3s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.428631, total=   0.5s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.343321, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.433137, total=   0.4s
[CV] .. weights=uniform, n_neighbors=24, score=0.423815, total=   0.6s
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.386733, total=   0.3s
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.413779, total=   0.5s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.374480, total=   0.6s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.440114, total=   0.4s
[CV] .. weights=uniform, n_neighbors=24, score=0.511945, total=   0.2s
[CV] .. weights=uniform, n_neighbors=24, score=0.370763, total=   0.6s
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.469361, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.445410, total=   0.3s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.442948, total=   0.5s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.469852, total=   0.4s
[CV] .. weights=uniform, n_neighbors=24, score=0.451834, total=   0.5s
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.430672, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.478851, total=   0.3s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.430360, total=   0.7s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.479020, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] . weights=distance, n_neighbors=24, score=0.441020, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.510797, total=   0.3s
[CV] weights=distance, n_neighbors=24 ................................
[CV] . weights=distance, n_neighbors=24, score=0.475710, total=   0.3s
[CV] . weights=distance, n_neighbors=24, score=0.340426, total=   0.2s
[CV] weights=distance, n_neighbors=24 ................................
[CV] . weights=distance, n_neighbors=24, score=0.384752, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] weights=distance, n_neighbors=24 ................................
[CV] . weights=distance, n_neighbors=24, score=0.437133, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.458752, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.425202, total=   0.4s
[CV] weights=distance, n_neighbors=24 ................................
[CV] .. weights=uniform, n_neighbors=24, score=0.457852, total=   0.5s
[CV] . weights=distance, n_neighbors=24, score=0.373824, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.368268, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.426130, total=   0.5s
[CV] . weights=distance, n_neighbors=24, score=0.411716, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.345221, total=   0.6s
[CV] . weights=distance, n_neighbors=24, score=0.425663, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.388497, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.380963, total=   0.5s
[CV] . weights=distance, n_neighbors=24, score=0.514600, total=   0.2s
[CV] . weights=distance, n_neighbors=24, score=0.439234, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.452210, total=   0.3s
[CV] . weights=distance, n_neighbors=24, score=0.444089, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.468931, total=   0.5s
[CV] . weights=distance, n_neighbors=24, score=0.454194, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.484284, total=   0.2s
[CV] . weights=distance, n_neighbors=24, score=0.491899, total=   0.2s
[CV] . weights=distance, n_neighbors=24, score=0.427065, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.472866, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.518987, total=   0.2s
[CV] . weights=distance, n_neighbors=24, score=0.430446, total=   0.4s
[CV] . weights=distance, n_neighbors=24, score=0.461120, total=   0.4s






    



[Parallel(n_jobs=-1)]: Done 1344 out of 1344 | elapsed:   48.8s finished



In [35]:

    
pd.DataFrame(clf.cv_results_).sort_values(by="rank_test_score").head(10)









    Out[35]:






  
    
      
      mean_fit_time
      mean_score_time
      mean_test_score
      mean_train_score
      param_n_neighbors
      param_weights
      params
      rank_test_score
      split0_test_score
      split0_train_score
      ...
      split7_test_score
      split7_train_score
      split8_test_score
      split8_train_score
      split9_test_score
      split9_train_score
      std_fit_time
      std_score_time
      std_test_score
      std_train_score
    
  
  
    
      47
      0.017150
      0.361954
      0.430512
      0.999564
      24
      distance
      {'weights': 'distance', 'n_neighbors': 24}
      1
      0.384752
      0.999828
      ...
      0.426130
      0.999470
      0.368268
      0.999470
      0.373824
      0.999394
      0.004052
      0.089811
      0.042896
      0.000177
    
    
      45
      0.018556
      0.410671
      0.430352
      0.999564
      23
      distance
      {'weights': 'distance', 'n_neighbors': 23}
      2
      0.385860
      0.999828
      ...
      0.425505
      0.999470
      0.366188
      0.999470
      0.371635
      0.999394
      0.007555
      0.104556
      0.043277
      0.000177
    
    
      43
      0.018926
      0.396208
      0.429236
      0.999564
      22
      distance
      {'weights': 'distance', 'n_neighbors': 22}
      3
      0.386746
      0.999828
      ...
      0.419671
      0.999470
      0.364733
      0.999470
      0.369009
      0.999394
      0.005711
      0.085330
      0.042890
      0.000177
    
    
      46
      0.018159
      0.410786
      0.428439
      0.822311
      24
      uniform
      {'weights': 'uniform', 'n_neighbors': 24}
      4
      0.382314
      0.832989
      ...
      0.428631
      0.823987
      0.370763
      0.831168
      0.374480
      0.834848
      0.006637
      0.109979
      0.041346
      0.006673
    
    
      44
      0.018515
      0.392315
      0.428120
      0.827704
      23
      uniform
      {'weights': 'uniform', 'n_neighbors': 23}
      5
      0.382757
      0.840224
      ...
      0.427797
      0.830787
      0.370347
      0.836029
      0.372948
      0.840128
      0.006885
      0.095834
      0.042259
      0.006937
    
    
      42
      0.021325
      0.376307
      0.427499
      0.830179
      22
      uniform
      {'weights': 'uniform', 'n_neighbors': 22}
      6
      0.385860
      0.842722
      ...
      0.425714
      0.832377
      0.369723
      0.837090
      0.372292
      0.842811
      0.009109
      0.091047
      0.040899
      0.006616
    
    
      41
      0.020516
      0.391431
      0.427207
      0.999564
      21
      distance
      {'weights': 'distance', 'n_neighbors': 21}
      7
      0.386303
      0.999828
      ...
      0.418629
      0.999470
      0.362030
      0.999470
      0.370103
      0.999394
      0.007498
      0.088926
      0.042559
      0.000177
    
    
      39
      0.019675
      0.368868
      0.427163
      0.999564
      20
      distance
      {'weights': 'distance', 'n_neighbors': 20}
      8
      0.389849
      0.999828
      ...
      0.415712
      0.999470
      0.362445
      0.999470
      0.369009
      0.999394
      0.006048
      0.080848
      0.042518
      0.000177
    
    
      40
      0.019284
      0.374979
      0.427127
      0.835023
      21
      uniform
      {'weights': 'uniform', 'n_neighbors': 21}
      9
      0.383200
      0.848320
      ...
      0.425922
      0.837764
      0.367644
      0.842305
      0.372510
      0.847486
      0.007511
      0.118639
      0.041051
      0.006892
    
    
      38
      0.020700
      0.359920
      0.425612
      0.839442
      20
      uniform
      {'weights': 'uniform', 'n_neighbors': 20}
      10
      0.386525
      0.853058
      ...
      0.425922
      0.842710
      0.367852
      0.847609
      0.370759
      0.851554
      0.009574
      0.097203
      0.039933
      0.006822
    
  

10 rows × 68 columns

It looks like several different parameter combinations give the same score. We'll pick the one using the most neighbours: n_neighbors=1 and weights="distance" (the latter has no effect with k=1)



In [36]:

    
clf_knn = neighbors.KNeighborsClassifier(24, weights="distance")
clf_knn.fit(X_train, y_train)
predicted_labels = clf_knn.predict(X_test)

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS   126     6     2                 1                     135
     CSiS     3   310    30                                       343
     FSiS     3    22   262                 1           1         289
     SiSh                 8    91     3     4                     106
       MS                 1     5    87    14     2     1         110
       WS           2           7     9   197     3    10     1   229
        D                 2     1     2    11    49    15     2    82
       PS                 2     2     2     7     7   205     3   228
       BS                             1           2    12    76    91

Precision  0.95  0.91  0.85  0.86  0.84  0.84  0.78  0.84  0.93  0.87
   Recall  0.93  0.90  0.91  0.86  0.79  0.86  0.60  0.90  0.84  0.87
       F1  0.94  0.91  0.88  0.86  0.81  0.85  0.68  0.87  0.88  0.87

Ensemble classifier



In [45]:

    
from sklearn.ensemble import VotingClassifier

eclf = VotingClassifier(estimators=[('SVM', clf_svm), 
                                    ('DecisionTree', clf_dt), 
                                    ('KNN', clf_knn), 
                                    ('RandomForest', clf_rf)
                                   ], 
                        voting='hard')

eclf.fit(X_train, y_train)
predicted_labels = eclf.predict(X_test)

conf = confusion_matrix(y_test, predicted_labels)
display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS   116    16     2                 1                     135
     CSiS     5   310    27                 1                     343
     FSiS     2    80   205     1           1                     289
     SiSh           1     8    88           9                     106
       MS           2     1     8    74    22     1     2         110
       WS           2          17     1   199     1     8     1   229
        D                 2     7          22    40     9     2    82
       PS           1     1     3     1    39     3   178     2   228
       BS                             1     5     2    19    64    91

Precision  0.94  0.75  0.83  0.71  0.96  0.67  0.85  0.82  0.93  0.81
   Recall  0.86  0.90  0.71  0.83  0.67  0.87  0.49  0.78  0.70  0.79
       F1  0.90  0.82  0.77  0.77  0.79  0.75  0.62  0.80  0.80  0.79



In [46]:

    
display_adj_cm(conf, facies_labels, adjacent_facies, display_metrics=True, hide_zeros=True)









    



     Pred    SS  CSiS  FSiS  SiSh    MS    WS     D    PS    BS Total
     True
       SS   132           2                 1                     135
     CSiS         342                       1                     343
     FSiS     2         285     1           1                     289
     SiSh           1     8    88           9                     106
       MS           2     1         104           1     2         110
       WS           2          17         209                 1   229
        D                 2     7                71           2    82
       PS           1     1     3     1               222         228
       BS                             1     5                85    91

Precision  0.99  0.98  0.95  0.76  0.98  0.92  0.99  0.99  0.97  0.95
   Recall  0.98  1.00  0.99  0.83  0.95  0.91  0.87  0.97  0.93  0.95
       F1  0.98  0.99  0.97  0.79  0.96  0.92  0.92  0.98  0.95  0.95

Average test F1 score with leave 2 wells out



In [51]:

    
f1_eclf = []
lpgo = LeavePGroupsOut(n_groups=2)

for train, test in lpgo.split(scaled_features, correct_facies_labels, groups=well_names):  
    eclf.fit(scaled_features[train], correct_facies_labels[train])
    pred = eclf.predict(scaled_features[test])
    sc = f1_score(correct_facies_labels[test], pred, labels=np.arange(10), average='micro')
    
    well_name = set(well_names[test])
    print("{}  {:.3f}".format(well_name, sc))
    f1_eclf.append(sc)
    
#     conf = confusion_matrix(correct_facies_labels[test], pred)
#     display_cm(conf, facies_labels, display_metrics=True, hide_zeros=True)
#     print("")
    
print("Average leave-one-well-out F1 Score: %6f" % (sum(f1_eclf)/(1.0*(len(f1_eclf)))))









    



{nan, 'CROSS H CATTLE', 'NEWBY', 'CHURCHMAN BIBLE'}  0.453
{nan, 'LUKE G U', 'NEWBY', 'CHURCHMAN BIBLE'}  0.548
{nan, 'CHURCHMAN BIBLE', 'NEWBY', 'Recruit F9'}  0.480
{nan, 'CROSS H CATTLE', 'NOLAN', 'NEWBY', 'CHURCHMAN BIBLE'}  0.494
{nan, 'CHURCHMAN BIBLE', 'NOLAN', 'NEWBY', 'Recruit F9'}  0.377
{nan, 'CHURCHMAN BIBLE', 'NEWBY', 'SHANKLE'}  0.497
{nan, 'CHURCHMAN BIBLE', 'SHRIMPLIN', 'NEWBY'}  0.538
{nan, 'LUKE G U', 'CROSS H CATTLE'}  0.517
{nan, 'CROSS H CATTLE', 'NEWBY', 'Recruit F9'}  0.444
{nan, 'CROSS H CATTLE', 'NOLAN'}  0.431
{nan, 'CROSS H CATTLE', 'NOLAN', 'Recruit F9'}  0.437
{nan, 'CROSS H CATTLE', 'SHANKLE'}  0.380
{nan, 'CROSS H CATTLE', 'SHRIMPLIN'}  0.481
{'LUKE G U', 'NEWBY', 'Recruit F9'}  0.418
{'LUKE G U', 'NOLAN', 'CROSS H CATTLE'}  0.497
{'LUKE G U', 'NOLAN', 'Recruit F9'}  0.565
{'LUKE G U', 'SHANKLE'}  0.499
{'LUKE G U', 'SHRIMPLIN'}  0.505
{'CROSS H CATTLE', 'NOLAN', 'NEWBY', 'Recruit F9'}  0.477
{'NOLAN', 'NEWBY', 'Recruit F9'}  0.454
{'NEWBY', 'SHANKLE', 'Recruit F9'}  0.475
{'SHRIMPLIN', 'NEWBY', 'Recruit F9'}  0.472
{'CROSS H CATTLE', 'NOLAN', 'Recruit F9'}  0.499
{'CROSS H CATTLE', 'NOLAN', 'SHANKLE'}  0.479
{'CROSS H CATTLE', 'SHRIMPLIN', 'NOLAN'}  0.456
{'NOLAN', 'SHANKLE', 'Recruit F9'}  0.475
{'SHRIMPLIN', 'NOLAN', 'Recruit F9'}  0.545
{'SHRIMPLIN', 'SHANKLE'}  0.489
Average leave-one-well-out F1 Score: 0.477976

Prediction

Let's use the full dataset to train the models and make the prediction



In [52]:

    
#Load testing data and standardise
test_data = pd.read_csv('../validation_data_nofacies.csv')
test_features = test_data.drop(['Well Name', 'Depth', "Formation"], axis=1)
scaled_test_features = scaler.transform(test_features)



In [53]:

    
eclf.fit(scaled_features, correct_facies_labels)
predicted_test_labels = eclf.predict(scaled_test_features)

# Save predicted labels
test_data['Facies'] = predicted_test_labels
test_data.to_csv('Anjum48_Prediction_Submission_v2.csv')



In [54]:

    
# Plot predicted labels
make_facies_log_plot(
    test_data[test_data['Well Name'] == 'STUART'],
    facies_colors=facies_colors)

make_facies_log_plot(
    test_data[test_data['Well Name'] == 'CRAWFORD'],
    facies_colors=facies_colors)
mpl.rcParams.update(inline_rc)

Interestingly in the test wells, there appears to be some bad data where the logs appear to be linearly interpolated, e.g. ~3025mMD in Crawford

Future work/suggestions

Get the TensorFlow models to work in the VotingClassifier
Try a LSTM model using TensorFlow and use previous depths as features
Try some of the interesting feature engineering solutions shown by others (e.g. Paolo Bestagini's work, wavelet transforms etc.)
Use a normalised GR - this requires a bit more info on the geology to find a fieldwide correlatable high (shale) and low (sand/carbonate) GR events
Check for hydrocarbon bearing intervals and split the data into gas/oil and water zones. Alternatively, calculate water saturation using Archie's equation and use that as a feature (assume m = n = 2)
Detailed log QC removing intervals that look like they have suspicious data
Try incorporating the wells with missing PEF data. Some people have used a regression technique to predict this, but given the physics of the measurement this may/may not be appropriate
Try using TPOT or tuning XGBoost



In [ ]:

	Facies	Formation	Well Name	Depth	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
0	3	A1 SH	SHRIMPLIN	2793.0	77.450	0.664	9.900	11.915	4.600	1	1.000
1	3	A1 SH	SHRIMPLIN	2793.5	78.260	0.661	14.200	12.565	4.100	1	0.979
2	3	A1 SH	SHRIMPLIN	2794.0	79.050	0.658	14.800	13.050	3.600	1	0.957
3	3	A1 SH	SHRIMPLIN	2794.5	86.100	0.655	13.900	13.115	3.500	1	0.936
4	3	A1 SH	SHRIMPLIN	2795.0	74.580	0.647	13.500	13.300	3.400	1	0.915
...	...	...	...	...	...	...	...	...	...	...	...
4144	5	C LM	CHURCHMAN BIBLE	3120.5	46.719	0.947	1.828	7.254	3.617	2	0.685
4145	5	C LM	CHURCHMAN BIBLE	3121.0	44.563	0.953	2.241	8.013	3.344	2	0.677
4146	5	C LM	CHURCHMAN BIBLE	3121.5	49.719	0.964	2.925	8.013	3.190	2	0.669
4147	5	C LM	CHURCHMAN BIBLE	3122.0	51.469	0.965	3.083	7.708	3.152	2	0.661
4148	5	C LM	CHURCHMAN BIBLE	3122.5	50.031	0.970	2.609	6.668	3.295	2	0.653

	Facies	Depth	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
count	3232.000000	3232.000000	3232.000000	3232.000000	3232.000000	3232.000000	3232.000000	3232.000000	3232.000000
mean	4.422030	2875.824567	66.135769	0.642719	3.559642	13.483213	3.725014	1.498453	0.520287
std	2.504243	131.006274	30.854826	0.241845	5.228948	7.698980	0.896152	0.500075	0.286792
min	1.000000	2573.500000	13.250000	-0.025949	-21.832000	0.550000	0.200000	1.000000	0.010000
25%	2.000000	2791.000000	46.918750	0.492750	1.163750	8.346750	3.100000	1.000000	0.273000
50%	4.000000	2893.500000	65.721500	0.624437	3.500000	12.150000	3.551500	1.000000	0.526000
75%	6.000000	2980.000000	79.626250	0.812735	6.432500	16.453750	4.300000	2.000000	0.767250
max	9.000000	3122.500000	361.150000	1.480000	18.600000	84.400000	8.094000	2.000000	1.000000

	Facies	Depth	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
count	16122.000000	16122.000000	16122.000000	16122.000000	16122.000000	16122.000000	16122.000000	16122.000000	16122.000000
mean	4.420233	2875.779266	66.132765	0.642385	3.562926	13.488510	3.724287	1.498325	0.520044
std	2.492889	130.886683	30.795497	0.241821	5.207364	7.684145	0.894569	0.500013	0.284700
min	1.000000	2573.500000	12.759470	-0.026553	-21.923002	-1.112217	0.058033	1.000000	-0.096527
25%	2.000000	2791.000000	46.939636	0.490246	1.144971	8.359510	3.113000	1.000000	0.279000
50%	4.000000	2893.600003	65.816922	0.624654	3.461482	12.138445	3.546221	1.000000	0.525477
75%	6.000000	2980.000000	79.864096	0.812950	6.483459	16.492567	4.306479	2.000000	0.761796
max	9.000000	3122.500000	375.701116	1.487703	20.032203	84.606003	8.094000	2.000000	1.201765

	GR	ILD_log10	DeltaPHI	PHIND	PE	NM_M	RELPOS
0	77.450000	0.664000	9.900000	11.915000	4.600000	1	1.000000
1	79.284515	0.663545	11.150359	12.015714	4.546545	1	0.996012
2	79.937114	0.662987	12.190536	12.139619	4.460793	1	0.991895
3	79.753455	0.662357	13.035533	12.278167	4.351767	1	0.987673
4	79.079198	0.661685	13.700354	12.422809	4.228495	1	0.983367
...	...	...	...	...	...	...	...
16122	51.177622	0.964821	3.026104	7.597464	3.161609	2	0.659412
16123	50.791689	0.964974	2.945037	7.451181	3.179267	2	0.657823
16124	50.407644	0.965716	2.845018	7.256165	3.206420	2	0.656228
16125	50.121933	0.967306	2.731266	6.999433	3.244515	2	0.654622
16126	50.031000	0.970000	2.609000	6.668000	3.295000	2	0.653000

	mean_fit_time	mean_score_time	mean_test_score	mean_train_score	param_C	param_gamma	param_kernel	params	rank_test_score	split0_test_score	...	split7_test_score	split7_train_score	split8_test_score	split8_train_score	split9_test_score	split9_train_score	std_fit_time	std_score_time	std_test_score	std_train_score
14	7.158785	3.548661	0.476873	0.611961	5	0.01	rbf	{'gamma': 0.01, 'kernel': 'rbf', 'C': 5}	1	0.455230	...	0.526360	0.600548	0.468289	0.626182	0.430072	0.639314	1.146031	0.519848	0.039089	0.012052
54	15.052618	3.320419	0.475171	0.593774	10000	0.0001	rbf	{'gamma': 0.0001, 'kernel': 'rbf', 'C': 10000}	2	0.456782	...	0.525109	0.584474	0.463090	0.607973	0.418253	0.619926	2.938377	0.467400	0.043729	0.012762
37	8.427462	3.485163	0.474773	0.592844	100	0.001	rbf	{'gamma': 0.001, 'kernel': 'rbf', 'C': 100}	3	0.450798	...	0.528860	0.583591	0.462258	0.607531	0.422412	0.619233	1.491136	0.485869	0.044061	0.013014
20	7.136548	3.431708	0.474418	0.621774	10	0.01	rbf	{'gamma': 0.01, 'kernel': 'rbf', 'C': 10}	4	0.450576	...	0.526776	0.611499	0.468289	0.632193	0.430948	0.644681	1.133364	0.523610	0.039578	0.011432
43	11.131641	3.298738	0.473763	0.617477	1000	0.001	rbf	{'gamma': 0.001, 'kernel': 'rbf', 'C': 1000}	5	0.453901	...	0.526360	0.602049	0.472240	0.627243	0.427884	0.640007	2.080823	0.503587	0.039613	0.011837

	mean_fit_time	mean_score_time	mean_test_score	mean_train_score	param_max_depth	params	rank_test_score	split0_test_score	split0_train_score	split10_test_score	...	split7_test_score	split7_train_score	split8_test_score	split8_train_score	split9_test_score	split9_train_score	std_fit_time	std_score_time	std_test_score	std_train_score
3	0.087601	0.002639	0.454242	0.600811	5	{'max_depth': 5}	1	0.382979	0.628079	0.407904	...	0.495103	0.594984	0.438553	0.621851	0.394178	0.635939	0.016670	0.000414	0.046857	0.016661
5	0.108709	0.002626	0.449962	0.683652	7	{'max_depth': 7}	2	0.419548	0.707580	0.376853	...	0.466972	0.694516	0.405906	0.700963	0.399650	0.725266	0.010878	0.000403	0.044412	0.014936
4	0.098397	0.002562	0.446364	0.641523	6	{'max_depth': 6}	3	0.414229	0.660637	0.401553	...	0.451969	0.644529	0.421501	0.656767	0.346465	0.681555	0.013679	0.000392	0.051968	0.014949
2	0.075952	0.003243	0.445336	0.558320	4	{'max_depth': 4}	4	0.393617	0.581826	0.346860	...	0.465930	0.549501	0.372427	0.565367	0.394397	0.601143	0.018185	0.003917	0.054661	0.017602
7	0.154772	0.002851	0.442784	0.770599	9	{'max_depth': 9}	5	0.395390	0.791301	0.395907	...	0.447385	0.791398	0.401539	0.796340	0.343839	0.804034	0.027358	0.000541	0.040286	0.015717

	mean_fit_time	mean_score_time	mean_test_score	mean_train_score	param_bootstrap	param_criterion	param_max_depth	param_max_features	param_min_samples_leaf	params	...	split7_test_score	split7_train_score	split8_test_score	split8_train_score	split9_test_score	split9_train_score	std_fit_time	std_score_time	std_test_score	std_train_score
11	4.558404	0.225488	0.496092	0.890204	True	gini	None	1	10	{'max_features': 1, 'criterion': 'gini', 'max_...	...	0.501146	0.894286	0.440632	0.889331	0.401620	0.896564	0.397693	0.047657	0.054994	0.006068
29	15.797385	0.242615	0.493505	0.893064	True	entropy	None	1	10	{'max_features': 1, 'criterion': 'entropy', 'm...	...	0.498229	0.891460	0.438345	0.892778	0.400306	0.900113	1.739411	0.061737	0.054174	0.005962
47	7.021959	0.230678	0.493328	0.931486	False	gini	None	1	10	{'max_features': 1, 'criterion': 'gini', 'max_...	...	0.497812	0.932615	0.430027	0.928578	0.413438	0.936986	0.632567	0.045544	0.054194	0.004293
10	5.130352	0.238740	0.492450	0.973722	True	gini	None	1	3	{'max_features': 1, 'criterion': 'gini', 'max_...	...	0.505522	0.976155	0.418590	0.972686	0.396805	0.976024	0.407751	0.038363	0.054357	0.001549
65	25.445620	0.245247	0.491015	0.934640	False	entropy	None	1	10	{'max_features': 1, 'criterion': 'entropy', 'm...	...	0.500313	0.934823	0.419214	0.930788	0.404027	0.940708	2.282686	0.068402	0.054038	0.004525

	mean_fit_time	mean_score_time	mean_test_score	mean_train_score	param_n_neighbors	param_weights	params	rank_test_score	split0_test_score	split0_train_score	...	split7_test_score	split7_train_score	split8_test_score	split8_train_score	split9_test_score	split9_train_score	std_fit_time	std_score_time	std_test_score	std_train_score
47	0.017150	0.361954	0.430512	0.999564	24	distance	{'weights': 'distance', 'n_neighbors': 24}	1	0.384752	0.999828	...	0.426130	0.999470	0.368268	0.999470	0.373824	0.999394	0.004052	0.089811	0.042896	0.000177
45	0.018556	0.410671	0.430352	0.999564	23	distance	{'weights': 'distance', 'n_neighbors': 23}	2	0.385860	0.999828	...	0.425505	0.999470	0.366188	0.999470	0.371635	0.999394	0.007555	0.104556	0.043277	0.000177
43	0.018926	0.396208	0.429236	0.999564	22	distance	{'weights': 'distance', 'n_neighbors': 22}	3	0.386746	0.999828	...	0.419671	0.999470	0.364733	0.999470	0.369009	0.999394	0.005711	0.085330	0.042890	0.000177
46	0.018159	0.410786	0.428439	0.822311	24	uniform	{'weights': 'uniform', 'n_neighbors': 24}	4	0.382314	0.832989	...	0.428631	0.823987	0.370763	0.831168	0.374480	0.834848	0.006637	0.109979	0.041346	0.006673
44	0.018515	0.392315	0.428120	0.827704	23	uniform	{'weights': 'uniform', 'n_neighbors': 23}	5	0.382757	0.840224	...	0.427797	0.830787	0.370347	0.836029	0.372948	0.840128	0.006885	0.095834	0.042259	0.006937
42	0.021325	0.376307	0.427499	0.830179	22	uniform	{'weights': 'uniform', 'n_neighbors': 22}	6	0.385860	0.842722	...	0.425714	0.832377	0.369723	0.837090	0.372292	0.842811	0.009109	0.091047	0.040899	0.006616
41	0.020516	0.391431	0.427207	0.999564	21	distance	{'weights': 'distance', 'n_neighbors': 21}	7	0.386303	0.999828	...	0.418629	0.999470	0.362030	0.999470	0.370103	0.999394	0.007498	0.088926	0.042559	0.000177
39	0.019675	0.368868	0.427163	0.999564	20	distance	{'weights': 'distance', 'n_neighbors': 20}	8	0.389849	0.999828	...	0.415712	0.999470	0.362445	0.999470	0.369009	0.999394	0.006048	0.080848	0.042518	0.000177
40	0.019284	0.374979	0.427127	0.835023	21	uniform	{'weights': 'uniform', 'n_neighbors': 21}	9	0.383200	0.848320	...	0.425922	0.837764	0.367644	0.842305	0.372510	0.847486	0.007511	0.118639	0.041051	0.006892
38	0.020700	0.359920	0.425612	0.839442	20	uniform	{'weights': 'uniform', 'n_neighbors': 20}	10	0.386525	0.853058	...	0.425922	0.842710	0.367852	0.847609	0.370759	0.851554	0.009574	0.097203	0.039933	0.006822