Author: Anjum Sayed. My prediction approach is as follows:
There are many ways to improve on my method. See the future work at the end section for ideas.
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:
The nine discrete facies (classes of rocks) are:
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)
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
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)
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
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
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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[Parallel(n_jobs=-1)]: Done 80 tasks | elapsed: 2.3min
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[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 .....................................
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[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 .....................................
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[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 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[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 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[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 .................................
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[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 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.001, kernel=rbf, C=5000 .................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[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 ..................................
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[CV] .. gamma=0.001, kernel=rbf, C=5000, score=0.476222, total= 20.7s
[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[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 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
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[CV] gamma=0.01, kernel=rbf, C=5000 ..................................
[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.478641, total= 47.2s
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[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.475206, total= 49.4s
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[CV] ... gamma=0.01, kernel=rbf, C=5000, score=0.383235, total= 42.1s
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[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
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 ....................................................
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[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
[CV] ..................... max_depth=17, score=0.340367, total= 0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=19, score=0.408163, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=19, score=0.458524, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=19, score=0.414398, total= 0.2s
[CV] ..................... max_depth=18, score=0.429200, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=20, score=0.357979, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=18, score=0.366728, total= 0.3s
[CV] ..................... max_depth=18, score=0.357762, total= 0.2s
[CV] max_depth=18 ....................................................
[CV] ..................... max_depth=21, score=0.468727, total= 0.2s
[CV] ..................... max_depth=18, score=0.434882, total= 0.2s
[CV] ..................... max_depth=21, score=0.400044, total= 0.3s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=17, score=0.409555, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=21 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.327205, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=18, score=0.365076, total= 0.2s
[CV] max_depth=18 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=19, score=0.354404, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=19, score=0.411561, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=20, score=0.432382, total= 0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=20, score=0.415600, total= 0.2s
[CV] ..................... max_depth=20, score=0.394031, total= 0.2s
[CV] ..................... max_depth=22, score=0.303404, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=21, score=0.404896, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=19, score=0.355349, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=21, score=0.453925, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=22, score=0.419195, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=19, score=0.439050, total= 0.2s
[CV] ..................... max_depth=22, score=0.404888, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=20, score=0.414335, total= 0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=19, score=0.347788, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=21, score=0.368161, total= 0.2s
[CV] ..................... max_depth=22, score=0.390253, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.383141, total= 0.2s
[CV] ..................... max_depth=20, score=0.359790, total= 0.2s
[CV] ..................... max_depth=23, score=0.386525, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=22, score=0.456170, total= 0.2s
[CV] ..................... max_depth=21, score=0.424135, total= 0.2s
[CV] ..................... max_depth=20, score=0.391557, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=19, score=0.385802, total= 0.2s
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=18, score=0.412883, total= 0.2s
[CV] ..................... max_depth=22, score=0.435018, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=22, score=0.410167, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] max_depth=23 ....................................................
[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 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=19, score=0.431898, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=19, score=0.401955, total= 0.2s
[CV] ..................... max_depth=22, score=0.331927, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=20, score=0.452345, total= 0.3s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=20, score=0.389963, total= 0.2s
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=21, score=0.404447, total= 0.3s
[CV] ..................... max_depth=19, score=0.376258, total= 0.2s
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=22, score=0.388620, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] ..................... max_depth=22, score=0.381871, total= 0.2s
[CV] ..................... max_depth=22, score=0.419991, total= 0.2s
[CV] ..................... max_depth=22, score=0.446571, total= 0.3s
[CV] max_depth=22 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=23, score=0.398531, total= 0.1s
[CV] max_depth=23 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.314894, total= 0.3s
[CV] max_depth=21 ....................................................
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=23, score=0.479148, total= 0.2s
[CV] ..................... max_depth=23, score=0.316699, total= 0.2s
[CV] ..................... max_depth=20, score=0.465301, total= 0.2s
[CV] ..................... max_depth=22, score=0.470898, total= 0.2s
[CV] ..................... max_depth=19, score=0.405097, total= 0.2s
[CV] max_depth=23 ....................................................
[CV] max_depth=22 ....................................................
[CV] max_depth=19 ....................................................
[CV] ..................... max_depth=20, score=0.378460, total= 0.2s
[CV] max_depth=23 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=20 ....................................................
[CV] ..................... max_depth=23, score=0.431750, total= 0.2s
[CV] ..................... max_depth=22, score=0.444899, total= 0.2s
[CV] max_depth=22 ....................................................
[CV] ..................... max_depth=21, score=0.338257, total= 0.2s
[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 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=22, score=0.420586, total= 0.2s
[CV] ..................... max_depth=21, score=0.450353, total= 0.3s
[CV] max_depth=23 ....................................................
[CV] max_depth=22 ....................................................
[CV] max_depth=21 ....................................................
[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 ....................................................
[CV] max_depth=23 ....................................................
[CV] ..................... max_depth=21, score=0.435253, total= 0.2s
[CV] max_depth=21 ....................................................
[CV] max_depth=23 ....................................................
[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 ....................................................
[CV] ..................... max_depth=23, score=0.324681, total= 0.2s
[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 ....................................................
[CV] max_depth=20 ....................................................
[CV] max_depth=24 ....................................................
[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
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[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
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[CV] max_depth=22 ....................................................
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[CV] max_depth=21 ....................................................
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[CV] max_depth=23 ....................................................
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[CV] ..................... max_depth=20, score=0.400278, total= 0.2s
[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=20, score=0.324541, total= 0.2s
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[CV] max_depth=24 ....................................................
[CV] ..................... max_depth=24, score=0.388646, total= 0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
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[CV] ..................... max_depth=24, score=0.454289, total= 0.3s
[CV] max_depth=24 ....................................................
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[CV] max_depth=25 ....................................................
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[CV] max_depth=25 ....................................................
[CV] ..................... max_depth=22, score=0.388005, total= 0.2s
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[CV] ..................... max_depth=23, score=0.370989, total= 0.2s
[CV] max_depth=25 ....................................................
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[CV] max_depth=23 ....................................................
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[CV] ..................... max_depth=24, score=0.384530, total= 0.2s
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[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
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[CV] ..................... max_depth=22, score=0.319545, total= 0.2s
[CV] ..................... max_depth=21, score=0.438633, total= 0.2s
[CV] max_depth=23 ....................................................
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[CV] ..................... max_depth=21, score=0.427810, total= 0.2s
[CV] ..................... max_depth=21, score=0.365754, total= 0.3s
[CV] max_depth=25 ....................................................
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[CV] max_depth=22 ....................................................
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[CV] max_depth=23 ....................................................
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[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
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[CV] ..................... max_depth=24, score=0.407004, total= 0.2s
[CV] max_depth=24 ....................................................
[CV] max_depth=24 ....................................................
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[CV] max_depth=25 ....................................................
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[CV] max_depth=25 ....................................................
[CV] max_depth=24 ....................................................
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[CV] max_depth=24 ....................................................
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[CV] ..................... max_depth=24, score=0.420857, total= 0.2s
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[CV] max_depth=25 ....................................................
[CV] max_depth=26 ....................................................
[CV] max_depth=26 ....................................................
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[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 ....................................................
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[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
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[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 ....................................................
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[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
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
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
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
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
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
VotingClassifier
In [ ]:
Content source: seg/2016-ml-contest
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