In [2]:
# -*- coding: utf-8 -*-
"""
Created on Tue May 10 17:19:14 2016
@author: Methinee
"""
import pandas as pd
import numpy as np
import pickle
import xlwt
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
book = xlwt.Workbook(encoding="utf-8")
sheet1 = book.add_sheet("Precission")
#sheet2 = book.add_sheet("Precission With Merge progpa")
#sheet3 = book.add_sheet("Precission With Merge nothing")
#----------------------Traning Data With Merging-----------------------
#df_file = pd.read_csv('../data/df_more20.csv',delimiter=",", skip_blank_lines = True,
# error_bad_lines=False)
df_file = pd.read_csv('../data/df_sub_more20_merge.csv',delimiter=",", skip_blank_lines = True,
error_bad_lines=False)
count_courseId = df_file["3COURSEID"].value_counts()
more20 = count_courseId
headers=list(df_file.columns.values)
subjects = []
countSub = 0
#Create dictionary of list subjects
for sub in df_file[headers[1]]:
if sub not in subjects:
subjects.append(sub)
countSub = countSub+1
#Get subject that more 20 enrollment
count = 0
subjects.sort()
precision_rf={}
df_precision = more20.copy()
list_allsub = df_file.columns[4:]
allSubject_df = pd.DataFrame(columns=[subjects],index=[list_allsub])
top10_df = pd.DataFrame(columns=[subjects])
for subject in subjects:
#Create new Dataframe
print subject
df_sub = df_file[df_file['3COURSEID'] == subject]
df_sub = df_sub.iloc[np.random.permutation(len(df_sub))]
count_enrollment = df_sub['3COURSEID'].value_counts()
#print "Number of %s enrollment: %s"%(subject,count_enrollment)
A = df_sub.as_matrix()
X_all = A[:,4:]
X_all = X_all.astype(np.int64, copy=False)
y_all = A[:,2]
y_all = y_all.astype(np.int64, copy=False)
X_gpa = A[:,6:]
X_gpa = X_gpa.astype(np.int64, copy=False)
y_gpa = A[:,2]
y_gpa = y_gpa.astype(np.int64, copy=False)
X_onlySubject = A[:,6:209]
X_onlySubject = X_onlySubject.astype(np.int64, copy=False)
y_onlySubject = A[:,2]
y_onlySubject = y_onlySubject.astype(np.int64, copy=False)
#Training data
forest = RandomForestClassifier(n_estimators=10, max_depth=None,
min_samples_split=1, random_state=None, max_features=None)
clf_all = forest.fit(X_all, y_all)
scores_all = cross_val_score(clf_all, X_all, y_all, cv=5)
print scores_all
print "Random Forest Cross Validation of %s: %s"%(subject,scores_all.mean())
clf_gpa = forest.fit(X_gpa, y_gpa)
scores_gpa = cross_val_score(clf_gpa, X_gpa, y_gpa, cv=5)
print scores_gpa
print "Random Forest Cross Validation of %s: %s"%(subject,scores_gpa.mean())
clf_onlySubject = forest.fit(X_onlySubject, y_onlySubject)
scores_onlySubject = cross_val_score(clf_onlySubject, X_onlySubject, y_onlySubject, cv=5)
print scores_onlySubject
print "Random Forest Cross Validation of %s: %s"%(subject,scores_onlySubject.mean())
#precision_rf[subject] = scores_all.mean()
#df_precision.loc[subject]=precision_rf[subject]
precission_all_mean = scores_all.mean()
precission_gpa_mean = scores_gpa.mean()
precission_onlySubject_mean = scores_onlySubject.mean()
sheet1.write(count, 0, subject)
sheet1.write(count,1, precission_all_mean)
sheet1.write(count,3, precission_gpa_mean)
sheet1.write(count,5, precission_onlySubject_mean)
book.save("compare_precission.xls")
count = count+1
AT316
[ 0.61290323 0.56666667 0.60714286 0.59259259 0.85185185]
Random Forest Cross Validation of AT316: 0.646231438812
[ 0.5483871 0.63333333 0.64285714 0.66666667 0.66666667]
Random Forest Cross Validation of AT316: 0.63158218126
[ 0.58064516 0.7 0.64285714 0.62962963 0.7037037 ]
Random Forest Cross Validation of AT316: 0.651367127496
AT326
[ 0.54347826 0.53333333 0.72727273 0.65116279 0.58139535]
Random Forest Cross Validation of AT326: 0.607328492202
[ 0.52173913 0.57777778 0.75 0.72093023 0.58139535]
Random Forest Cross Validation of AT326: 0.630368497922
[ 0.5 0.53333333 0.63636364 0.69767442 0.6744186 ]
Random Forest Cross Validation of AT326: 0.608357998591
BA291
[ 0.47058824 0.5 0.53846154 0.38461538 0.41666667]
Random Forest Cross Validation of BA291: 0.462066365008
[ 0.29411765 0.57142857 0.46153846 0.38461538 0.41666667]
Random Forest Cross Validation of BA291: 0.425673346262
[ 0.41176471 0.42857143 0.46153846 0.46153846 0.5 ]
Random Forest Cross Validation of BA291: 0.452682611506
CJ315
[ 0.57142857 0.6 0.8 0.6 0.5 ]
Random Forest Cross Validation of CJ315: 0.614285714286
[ 0.57142857 0.6 0.8 0.6 0.75 ]
Random Forest Cross Validation of CJ315: 0.664285714286
[ 0.57142857 0.8 0.8 0.8 0.75 ]
Random Forest Cross Validation of CJ315: 0.744285714286
CJ316
[ 0.55555556 0.5 0.5 0.66666667 0.8 ]
Random Forest Cross Validation of CJ316: 0.604444444444
[ 0.44444444 0.5 0.625 0.5 1. ]
Random Forest Cross Validation of CJ316: 0.613888888889
[ 0.55555556 0.375 0.375 0.83333333 0.8 ]
Random Forest Cross Validation of CJ316: 0.587777777778
CJ317
[ 0.8 0.57142857 1. 0.71428571 0.83333333]
Random Forest Cross Validation of CJ317: 0.78380952381
[ 0.7 0.57142857 0.85714286 0.71428571 0.83333333]
Random Forest Cross Validation of CJ317: 0.735238095238
[ 0.7 0.71428571 1. 0.57142857 0.83333333]
Random Forest Cross Validation of CJ317: 0.76380952381
CJ321
[ 0.6 0.75 0.625 0.75 0.71428571]
Random Forest Cross Validation of CJ321: 0.687857142857
[ 0.6 0.75 0.5 0.625 0.71428571]
Random Forest Cross Validation of CJ321: 0.637857142857
[ 0.5 0.875 0.5 0.625 0.71428571]
Random Forest Cross Validation of CJ321: 0.642857142857
CS101
[ 0.421875 0.42105263 0.46031746 0.46560847 0.45744681]
Random Forest Cross Validation of CS101: 0.445260073203
[ 0.46354167 0.45789474 0.50793651 0.48148148 0.45744681]
Random Forest Cross Validation of CS101: 0.473660240287
[ 0.53125 0.53684211 0.53968254 0.52910053 0.53191489]
Random Forest Cross Validation of CS101: 0.533758013533
CS102
[ 0.36410256 0.42051282 0.37628866 0.35416667 0.36315789]
Random Forest Cross Validation of CS102: 0.375645721163
[ 0.33846154 0.35897436 0.3814433 0.38541667 0.34736842]
Random Forest Cross Validation of CS102: 0.362332856825
[ 0.43076923 0.41025641 0.41752577 0.41145833 0.42631579]
Random Forest Cross Validation of CS102: 0.419265107406
CS105
[ 0.29032258 0.29508197 0.38333333 0.43103448 0.42105263]
Random Forest Cross Validation of CS105: 0.364164999106
[ 0.11290323 0.26229508 0.28333333 0.24137931 0.26315789]
Random Forest Cross Validation of CS105: 0.232613769238
[ 0.30645161 0.31147541 0.3 0.31034483 0.31578947]
Random Forest Cross Validation of CS105: 0.308812264802
CS111
[ 0.51834862 0.59534884 0.56807512 0.58962264 0.62264151]
Random Forest Cross Validation of CS111: 0.578807345875
[ 0.45412844 0.53023256 0.55399061 0.51886792 0.5754717 ]
Random Forest Cross Validation of CS111: 0.526538246295
[ 0.47706422 0.53023256 0.5399061 0.53301887 0.56603774]
Random Forest Cross Validation of CS111: 0.529251897077
CS115
[ 0.85714286 0.90625 0.93333333 0.96551724 0.96551724]
Random Forest Cross Validation of CS115: 0.925552134647
[ 0.82857143 0.84375 0.8 0.86206897 0.86206897]
Random Forest Cross Validation of CS115: 0.839291871921
[ 0.77142857 0.84375 0.63333333 0.82758621 0.86206897]
Random Forest Cross Validation of CS115: 0.787633415435
CS211
[ 0.51724138 0.51851852 0.22222222 0.48 0.5 ]
Random Forest Cross Validation of CS211: 0.44759642401
[ 0.65517241 0.40740741 0.25925926 0.44 0.375 ]
Random Forest Cross Validation of CS211: 0.427367816092
[ 0.5862069 0.44444444 0.25925926 0.48 0.45833333]
Random Forest Cross Validation of CS211: 0.445648786718
CS213
[ 0.47096774 0.49350649 0.50657895 0.51973684 0.5 ]
Random Forest Cross Validation of CS213: 0.498158004983
[ 0.43870968 0.46753247 0.53289474 0.45394737 0.50657895]
Random Forest Cross Validation of CS213: 0.479932639517
[ 0.43870968 0.44805195 0.52631579 0.49342105 0.51315789]
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
Random Forest Cross Validation of CS213: 0.483931272463
CS214
[ 0.46534653 0.4950495 0.38383838 0.51578947 0.5106383 ]
Random Forest Cross Validation of CS214: 0.474132439
[ 0.43564356 0.54455446 0.48484848 0.53684211 0.5 ]
Random Forest Cross Validation of CS214: 0.500377721983
[ 0.41584158 0.52475248 0.42424242 0.51578947 0.55319149]
Random Forest Cross Validation of CS214: 0.486763489339
CS215
[ 0.33333333 0.44444444 0.5 0.5 0.5 ]
Random Forest Cross Validation of CS215: 0.455555555556
[ 0.5 0.5 0.4375 0.57142857 0.71428571]
Random Forest Cross Validation of CS215: 0.544642857143
[ 0.38888889 0.55555556 0.5625 0.64285714 0.57142857]
Random Forest Cross Validation of CS215: 0.544246031746
CS222
[ 0.51485149 0.48514851 0.48484848 0.50515464 0.51578947]
Random Forest Cross Validation of CS222: 0.501158519542
[ 0.52475248 0.58415842 0.54545455 0.54639175 0.51578947]
Random Forest Cross Validation of CS222: 0.543309332561
[ 0.56435644 0.54455446 0.50505051 0.55670103 0.48421053]
Random Forest Cross Validation of CS222: 0.530974590677
CS223
[ 0.55147059 0.55970149 0.53383459 0.54198473 0.58461538]
Random Forest Cross Validation of CS223: 0.554321356936
[ 0.50735294 0.57462687 0.43609023 0.54198473 0.56153846]
Random Forest Cross Validation of CS223: 0.524318645355
[ 0.52941176 0.58208955 0.55639098 0.53435115 0.47692308]
Random Forest Cross Validation of CS223: 0.53583330327
CS231
[ 0.96 1. 1. 1. 1. ]
Random Forest Cross Validation of CS231: 0.992
[ 0.96 1. 1. 1. 1. ]
Random Forest Cross Validation of CS231: 0.992
[ 0.96 1. 1. 1. 1. ]
Random Forest Cross Validation of CS231: 0.992
CS251
[ 0.71900826 0.60833333 0.6440678 0.68376068 0.62068966]
Random Forest Cross Validation of CS251: 0.655171946668
[ 0.6446281 0.56666667 0.60169492 0.68376068 0.61206897]
Random Forest Cross Validation of CS251: 0.621763866074
[ 0.6446281 0.55833333 0.54237288 0.65811966 0.63793103]
Random Forest Cross Validation of CS251: 0.608277001293
CS261
[ 0.52 0.55208333 0.53125 0.52631579 0.63829787]
Random Forest Cross Validation of CS261: 0.553589399029
[ 0.6 0.55208333 0.53125 0.48421053 0.57446809]
Random Forest Cross Validation of CS261: 0.548402388951
[ 0.48 0.54166667 0.5625 0.50526316 0.55319149]
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 3 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 4 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
Random Forest Cross Validation of CS261: 0.528524262785
CS281
[ 0.66666667 0.66666667 0.65546218 0.6440678 0.65217391]
Random Forest Cross Validation of CS281: 0.657007445572
[ 0.69166667 0.55 0.64705882 0.6779661 0.66956522]
Random Forest Cross Validation of CS281: 0.647251361856
[ 0.64166667 0.65 0.63865546 0.68644068 0.73043478]
Random Forest Cross Validation of CS281: 0.669439517885
CS284
[ 0.52 0.504 0.44715447 0.46721311 0.52066116]
Random Forest Cross Validation of CS284: 0.491805748665
[ 0.488 0.48 0.43902439 0.43442623 0.47107438]
Random Forest Cross Validation of CS284: 0.462504999983
[ 0.472 0.488 0.44715447 0.44262295 0.46280992]
Random Forest Cross Validation of CS284: 0.462517467944
CS285
[ 0.77777778 0.77777778 0.5 1. 0.75 ]
Random Forest Cross Validation of CS285: 0.761111111111
[ 0.77777778 0.44444444 0.66666667 0.8 0.25 ]
Random Forest Cross Validation of CS285: 0.587777777778
[ 0.55555556 0.44444444 0.16666667 0.8 0.25 ]
Random Forest Cross Validation of CS285: 0.443333333333
CS286
[ 0.5 0.5 0.66666667 0.8 0.61538462]
Random Forest Cross Validation of CS286: 0.61641025641
[ 0.44444444 0.625 0.6 0.66666667 0.69230769]
Random Forest Cross Validation of CS286: 0.605683760684
[ 0.66666667 0.5 0.73333333 0.66666667 0.69230769]
Random Forest Cross Validation of CS286: 0.651794871795
CS288
[ 0.58536585 0.47222222 0.57142857 0.62857143 0.48571429]
Random Forest Cross Validation of CS288: 0.548660472319
[ 0.43902439 0.38888889 0.51428571 0.37142857 0.4 ]
Random Forest Cross Validation of CS288: 0.422725512969
[ 0.53658537 0.41666667 0.57142857 0.48571429 0.54285714]
Random Forest Cross Validation of CS288: 0.510650406504
CS289
[ 0.48717949 0.55263158 0.47222222 0.54285714 0.58823529]
Random Forest Cross Validation of CS289: 0.528625145065
[ 0.51282051 0.55263158 0.55555556 0.48571429 0.67647059]
Random Forest Cross Validation of CS289: 0.556638504255
[ 0.48717949 0.57894737 0.52777778 0.6 0.76470588]
Random Forest Cross Validation of CS289: 0.591722103146
CS295
[ 0.38888889 0.47058824 0.4375 0.46666667 0.4 ]
Random Forest Cross Validation of CS295: 0.43272875817
[ 0.44444444 0.47058824 0.3125 0.46666667 0.33333333]
Random Forest Cross Validation of CS295: 0.405506535948
[ 0.38888889 0.41176471 0.25 0.46666667 0.13333333]
Random Forest Cross Validation of CS295: 0.330130718954
CS296
[ 0.4 0.39130435 0.39130435 0.36363636 0.28571429]
Random Forest Cross Validation of CS296: 0.366391869001
[ 0.4 0.47826087 0.39130435 0.5 0.23809524]
Random Forest Cross Validation of CS296: 0.401532091097
[ 0.36 0.47826087 0.56521739 0.40909091 0.33333333]
Random Forest Cross Validation of CS296: 0.429180500659
CS297
[ 0.41666667 0.63636364 0.63636364 0.8 0.66666667]
Random Forest Cross Validation of CS297: 0.631212121212
[ 0.41666667 0.54545455 0.54545455 0.5 0.66666667]
Random Forest Cross Validation of CS297: 0.534848484848
[ 0.33333333 0.45454545 0.63636364 0.5 0.77777778]
Random Forest Cross Validation of CS297: 0.540404040404
CS300
[ 0.65384615 0.7826087 0.77272727 0.90909091 0.95454545]
Random Forest Cross Validation of CS300: 0.814563697172
[ 0.61538462 0.73913043 0.77272727 0.90909091 0.86363636]
Random Forest Cross Validation of CS300: 0.779993919124
[ 0.76923077 0.7826087 0.77272727 0.95454545 0.90909091]
Random Forest Cross Validation of CS300: 0.837640620249
CS301
[ 0.61052632 0.6 0.63829787 0.61111111 0.63333333]
Random Forest Cross Validation of CS301: 0.618653726515
[ 0.55789474 0.63157895 0.59574468 0.57777778 0.5 ]
Random Forest Cross Validation of CS301: 0.572599228568
[ 0.58947368 0.57894737 0.59574468 0.57777778 0.58888889]
Random Forest Cross Validation of CS301: 0.58616648003
CS302
[ 0.65555556 0.66292135 0.65909091 0.73255814 0.64285714]
Random Forest Cross Validation of CS302: 0.670596619071
[ 0.58888889 0.69662921 0.70454545 0.68604651 0.64285714]
Random Forest Cross Validation of CS302: 0.663793442281
[ 0.65555556 0.69662921 0.75 0.72093023 0.69047619]
Random Forest Cross Validation of CS302: 0.702718238415
CS311
[ 0.5877193 0.61403509 0.71171171 0.65137615 0.67592593]
Random Forest Cross Validation of CS311: 0.648153634078
[ 0.59649123 0.56140351 0.66666667 0.65137615 0.66666667]
Random Forest Cross Validation of CS311: 0.628520843393
[ 0.57894737 0.5877193 0.62162162 0.58715596 0.64814815]
Random Forest Cross Validation of CS311: 0.604718479948
CS314
[ 0.34615385 0.33653846 0.37864078 0.46464646 0.39393939]
Random Forest Cross Validation of CS314: 0.383983788595
[ 0.375 0.43269231 0.33980583 0.43434343 0.42424242]
Random Forest Cross Validation of CS314: 0.401216798304
[ 0.36538462 0.29807692 0.38834951 0.49494949 0.42424242]
Random Forest Cross Validation of CS314: 0.394200594443
CS326
[ 0.61538462 0.64864865 0.63888889 0.61764706 0.54545455]
Random Forest Cross Validation of CS326: 0.61320475144
[ 0.64102564 0.54054054 0.58333333 0.67647059 0.42424242]
Random Forest Cross Validation of CS326: 0.573122505475
[ 0.56410256 0.51351351 0.61111111 0.58823529 0.60606061]
Random Forest Cross Validation of CS326: 0.576604617781
CS341
[ 0.69827586 0.67826087 0.72072072 0.65765766 0.7 ]
Random Forest Cross Validation of CS341: 0.690983022003
[ 0.63793103 0.68695652 0.68468468 0.64864865 0.69090909]
Random Forest Cross Validation of CS341: 0.669825996093
[ 0.6637931 0.73043478 0.65765766 0.67567568 0.69090909]
Random Forest Cross Validation of CS341: 0.68369406206
CS342
[ 0.75280899 0.70114943 0.73563218 0.74418605 0.78571429]
Random Forest Cross Validation of CS342: 0.743898186037
[ 0.64044944 0.57471264 0.65517241 0.61627907 0.66666667]
Random Forest Cross Validation of CS342: 0.630656046422
[ 0.69662921 0.62068966 0.65517241 0.6744186 0.69047619]
Random Forest Cross Validation of CS342: 0.667477215515
CS348
[ 0.33333333 0.36363636 0.71428571 0.2 1. ]
Random Forest Cross Validation of CS348: 0.522251082251
[ 0.25 0.36363636 0.42857143 0.6 0.6 ]
Random Forest Cross Validation of CS348: 0.448441558442
[ 0.41666667 0.36363636 0.57142857 0.6 0.8 ]
Random Forest Cross Validation of CS348: 0.550346320346
CS356
[ 0.64705882 0.52941176 0.66666667 0.64285714 0.84615385]
Random Forest Cross Validation of CS356: 0.666429648783
[ 0.47058824 0.47058824 0.4 0.64285714 0.69230769]
Random Forest Cross Validation of CS356: 0.535268261151
[ 0.58823529 0.52941176 0.46666667 0.42857143 0.76923077]
Random Forest Cross Validation of CS356: 0.556423184658
CS365
[ 0.48979592 0.45833333 0.55319149 0.53191489 0.60869565]
Random Forest Cross Validation of CS365: 0.528386257371
[ 0.59183673 0.47916667 0.42553191 0.59574468 0.56521739]
Random Forest Cross Validation of CS365: 0.531499477682
[ 0.53061224 0.47916667 0.42553191 0.53191489 0.60869565]
Random Forest Cross Validation of CS365: 0.51518427445
CS366
[ 0.58333333 0.68181818 0.61904762 0.68421053 0.66666667]
Random Forest Cross Validation of CS366: 0.647015265436
[ 0.625 0.59090909 0.66666667 0.47368421 0.72222222]
Random Forest Cross Validation of CS366: 0.615696438065
[ 0.75 0.54545455 0.61904762 0.63157895 0.66666667]
Random Forest Cross Validation of CS366: 0.642549555707
CS367
[ 0.54166667 0.54166667 0.41666667 0.63157895 0.57894737]
Random Forest Cross Validation of CS367: 0.542105263158
[ 0.33333333 0.58333333 0.41666667 0.47368421 0.52631579]
Random Forest Cross Validation of CS367: 0.466666666667
[ 0.45833333 0.45833333 0.33333333 0.47368421 0.47368421]
Random Forest Cross Validation of CS367: 0.439473684211
CS374
[ 0.50549451 0.69230769 0.56666667 0.57471264 0.70588235]
Random Forest Cross Validation of CS374: 0.609012772218
[ 0.46153846 0.52747253 0.52222222 0.5862069 0.6 ]
Random Forest Cross Validation of CS374: 0.539488021557
[ 0.47252747 0.54945055 0.55555556 0.6091954 0.63529412]
Random Forest Cross Validation of CS374: 0.564404619496
CS377
[ 0.5 0.44444444 0.4 0.69230769 0.58333333]
Random Forest Cross Validation of CS377: 0.524017094017
[ 0.44444444 0.55555556 0.46666667 0.46153846 0.33333333]
Random Forest Cross Validation of CS377: 0.452307692308
[ 0.33333333 0.55555556 0.6 0.53846154 0.5 ]
Random Forest Cross Validation of CS377: 0.50547008547
CS385
[ 0.44736842 0.64864865 0.61111111 0.66666667 0.55882353]
Random Forest Cross Validation of CS385: 0.586523675378
[ 0.39473684 0.54054054 0.75 0.69444444 0.58823529]
Random Forest Cross Validation of CS385: 0.593591424242
[ 0.47368421 0.62162162 0.55555556 0.63888889 0.64705882]
Random Forest Cross Validation of CS385: 0.587361820024
CS386
[ 0.35294118 0.47058824 0.41176471 0.6 0.23076923]
Random Forest Cross Validation of CS386: 0.413212669683
[ 0.52941176 0.47058824 0.23529412 0.46666667 0.53846154]
Random Forest Cross Validation of CS386: 0.448084464555
[ 0.23529412 0.47058824 0.47058824 0.46666667 0.38461538]
Random Forest Cross Validation of CS386: 0.405550527903
CS387
[ 0.30769231 0.45454545 0.63636364 0.6 0.44444444]
Random Forest Cross Validation of CS387: 0.488609168609
[ 0.23076923 0.45454545 0.54545455 0.6 0.66666667]
Random Forest Cross Validation of CS387: 0.499487179487
[ 0.46153846 0.45454545 0.54545455 0.8 0.55555556]
Random Forest Cross Validation of CS387: 0.563418803419
CS388
[ 0.6 0.6 0.625 0.75 0.85714286]
Random Forest Cross Validation of CS388: 0.686428571429
[ 0.5 0.6 0.375 0.75 0.71428571]
Random Forest Cross Validation of CS388: 0.587857142857
[ 0.7 0.4 0.375 0.75 0.42857143]
Random Forest Cross Validation of CS388: 0.530714285714
CS395
[ 0.57407407 0.53846154 0.54 0.46 0.66 ]
Random Forest Cross Validation of CS395: 0.554507122507
[ 0.53703704 0.57692308 0.58 0.4 0.66 ]
Random Forest Cross Validation of CS395: 0.550792022792
[ 0.61111111 0.57692308 0.62 0.42 0.6 ]
Random Forest Cross Validation of CS395: 0.565606837607
CS396
[ 0.28571429 0.41666667 0.63636364 0.5 0.77777778]
Random Forest Cross Validation of CS396: 0.523304473304
[ 0.42857143 0.25 0.45454545 0.5 0.77777778]
Random Forest Cross Validation of CS396: 0.482178932179
[ 0.21428571 0.33333333 0.54545455 0.5 0.77777778]
Random Forest Cross Validation of CS396: 0.47417027417
CS397
[ 0.46153846 0.58333333 0.7 0.88888889 0.625 ]
Random Forest Cross Validation of CS397: 0.651752136752
[ 0.30769231 0.5 0.5 0.55555556 0.25 ]
Random Forest Cross Validation of CS397: 0.42264957265
[ 0.15384615 0.33333333 0.6 0.55555556 0.375 ]
Random Forest Cross Validation of CS397: 0.403547008547
CS398
[ 0.38461538 0.36363636 0.5 0.55555556 0.5 ]
Random Forest Cross Validation of CS398: 0.460761460761
[ 0.38461538 0.18181818 0.6 0.33333333 0.5 ]
Random Forest Cross Validation of CS398: 0.399953379953
[ 0.23076923 0.27272727 0.6 0.33333333 0.375 ]
Random Forest Cross Validation of CS398: 0.362365967366
CS399
[ 0.66666667 0.6 0.6 0.75 1. ]
Random Forest Cross Validation of CS399: 0.723333333333
[ 0.66666667 0.6 0.2 0.5 0.75 ]
Random Forest Cross Validation of CS399: 0.543333333333
[ 0.66666667 0.8 0.6 0.5 0.75 ]
Random Forest Cross Validation of CS399: 0.663333333333
CS401
[ 0.73529412 0.73134328 0.68656716 0.78787879 0.76923077]
Random Forest Cross Validation of CS401: 0.742062824504
[ 0.72058824 0.67164179 0.7761194 0.78787879 0.70769231]
Random Forest Cross Validation of CS401: 0.732784104979
[ 0.70588235 0.64179104 0.73134328 0.6969697 0.73846154]
Random Forest Cross Validation of CS401: 0.702889583346
CS402
[ 0.59722222 0.66197183 0.62318841 0.69117647 0.68656716]
Random Forest Cross Validation of CS402: 0.652025218755
[ 0.59722222 0.57746479 0.62318841 0.52941176 0.6119403 ]
Random Forest Cross Validation of CS402: 0.587845495993
[ 0.625 0.6056338 0.55072464 0.54411765 0.65671642]
Random Forest Cross Validation of CS402: 0.596438501093
CS407
[ 0.5 0.5 0.57142857 0.5 0.5 ]
Random Forest Cross Validation of CS407: 0.514285714286
[ 0.75 0.625 0.71428571 0.83333333 0.33333333]
Random Forest Cross Validation of CS407: 0.65119047619
[ 0.75 0.625 0.57142857 0.66666667 0.66666667]
Random Forest Cross Validation of CS407: 0.655952380952
CS408
[ 0.57142857 0.66666667 0.8 0.8 0.5 ]
Random Forest Cross Validation of CS408: 0.667619047619
[ 0.57142857 0.66666667 0.8 0.6 0.5 ]
Random Forest Cross Validation of CS408: 0.627619047619
[ 0.57142857 0.66666667 0.8 0.8 0.75 ]
Random Forest Cross Validation of CS408: 0.717619047619
CS409
[ 0.5 0.5625 0.3125 0.3125 0.4 ]
Random Forest Cross Validation of CS409: 0.4175
[ 0.33333333 0.375 0.5 0.5625 0.26666667]
Random Forest Cross Validation of CS409: 0.4075
[ 0.27777778 0.375 0.3125 0.5625 0.26666667]
Random Forest Cross Validation of CS409: 0.358888888889
CS426
[ 0.55555556 0.375 0.2 0.4 0.4 ]
Random Forest Cross Validation of CS426: 0.386111111111
[ 0.33333333 0.25 0.6 0.8 0.6 ]
Random Forest Cross Validation of CS426: 0.516666666667
[ 0.33333333 0.375 0.4 0.6 0.4 ]
Random Forest Cross Validation of CS426: 0.421666666667
CS427
[ 0.35714286 0.69230769 0.54545455 0.33333333 0.55555556]
Random Forest Cross Validation of CS427: 0.496758796759
[ 0.42857143 0.46153846 0.63636364 0.55555556 0.66666667]
Random Forest Cross Validation of CS427: 0.549739149739
[ 0.42857143 0.38461538 0.45454545 0.55555556 0.22222222]
Random Forest Cross Validation of CS427: 0.409102009102
CS429
[ 0.64705882 0.75 0.66666667 0.78571429 0.76923077]
Random Forest Cross Validation of CS429: 0.723734109028
[ 0.64705882 0.625 0.6 0.64285714 0.53846154]
Random Forest Cross Validation of CS429: 0.61067550097
[ 0.70588235 0.75 0.66666667 0.64285714 0.46153846]
Random Forest Cross Validation of CS429: 0.645388924801
CS446
[ 0.58333333 0.27272727 0.55555556 0.55555556 0.55555556]
Random Forest Cross Validation of CS446: 0.504545454545
[ 0.58333333 0.45454545 0.44444444 0.77777778 0.44444444]
Random Forest Cross Validation of CS446: 0.540909090909
[ 0.33333333 0.27272727 0.55555556 0.44444444 0.44444444]
Random Forest Cross Validation of CS446: 0.410101010101
CS449
[ 0.83333333 0.5 0.83333333 0.83333333 0.83333333]
Random Forest Cross Validation of CS449: 0.766666666667
[ 1. 0.66666667 0.83333333 0.83333333 0.83333333]
Random Forest Cross Validation of CS449: 0.833333333333
[ 1. 0.33333333 1. 0.83333333 0.83333333]
Random Forest Cross Validation of CS449: 0.8
CS456
[ 0.26666667 0.53333333 0.57142857 0.38461538 0.36363636]
Random Forest Cross Validation of CS456: 0.423936063936
[ 0.33333333 0.53333333 0.42857143 0.53846154 0.54545455]
Random Forest Cross Validation of CS456: 0.475830835831
[ 0.53333333 0.53333333 0.42857143 0.61538462 0.54545455]
Random Forest Cross Validation of CS456: 0.531215451215
CS457
[ 0.57142857 0.57142857 0.28571429 0.66666667 0.4 ]
Random Forest Cross Validation of CS457: 0.499047619048
[ 0.42857143 0.57142857 0.42857143 0.33333333 0.6 ]
Random Forest Cross Validation of CS457: 0.472380952381
[ 0.71428571 0.42857143 0.71428571 0.33333333 0.4 ]
Random Forest Cross Validation of CS457: 0.518095238095
CS459
[ 0.76470588 0.875 0.8 0.86666667 0.66666667]
Random Forest Cross Validation of CS459: 0.794607843137
[ 0.58823529 0.4375 0.66666667 0.66666667 0.66666667]
Random Forest Cross Validation of CS459: 0.605147058824
[ 0.70588235 0.75 0.46666667 0.6 0.33333333]
Random Forest Cross Validation of CS459: 0.571176470588
CS467
[ 0.35294118 0.35714286 0.41666667 0.58333333 0.5 ]
Random Forest Cross Validation of CS467: 0.442016806723
[ 0.23529412 0.42857143 0.16666667 0.5 0.41666667]
Random Forest Cross Validation of CS467: 0.34943977591
[ 0.17647059 0.42857143 0.25 0.66666667 0.25 ]
Random Forest Cross Validation of CS467: 0.354341736695
CS486
[ 0.60869565 0.61904762 0.85714286 0.52631579 0.61111111]
Random Forest Cross Validation of CS486: 0.64446260579
[ 0.52173913 0.57142857 0.57142857 0.42105263 0.5 ]
Random Forest Cross Validation of CS486: 0.517129780974
[ 0.56521739 0.57142857 0.52380952 0.36842105 0.66666667]
Random Forest Cross Validation of CS486: 0.539108641168
CS487
[ 0.65 0.67567568 0.72222222 0.57142857 0.70588235]
Random Forest Cross Validation of CS487: 0.665041764454
[ 0.45 0.51351351 0.52777778 0.54285714 0.58823529]
Random Forest Cross Validation of CS487: 0.524476745653
[ 0.55 0.59459459 0.63888889 0.51428571 0.58823529]
Random Forest Cross Validation of CS487: 0.577200898377
CS488
[ 0.91304348 0.7826087 0.61904762 0.9047619 0.85714286]
Random Forest Cross Validation of CS488: 0.815320910973
[ 0.73913043 0.69565217 0.66666667 0.9047619 0.85714286]
Random Forest Cross Validation of CS488: 0.772670807453
[ 0.73913043 0.69565217 0.57142857 0.66666667 0.71428571]
Random Forest Cross Validation of CS488: 0.677432712215
CS489
[ 0.35714286 0.51851852 0.72 0.41666667 0.47826087]
Random Forest Cross Validation of CS489: 0.498117782379
[ 0.35714286 0.55555556 0.76 0.33333333 0.43478261]
Random Forest Cross Validation of CS489: 0.488162870945
[ 0.5 0.48148148 0.6 0.33333333 0.47826087]
Random Forest Cross Validation of CS489: 0.478615136876
EL070
[ 0.85416667 0.80434783 0.84782609 0.84782609 0.90909091]
Random Forest Cross Validation of EL070: 0.852651515152
[ 0.85416667 0.82608696 0.76086957 0.82608696 0.81818182]
Random Forest Cross Validation of EL070: 0.817078392622
[ 0.875 0.89130435 0.89130435 0.89130435 0.93181818]
Random Forest Cross Validation of EL070: 0.896146245059
EL171
[ 0.42944785 0.36419753 0.375 0.36708861 0.39102564]
Random Forest Cross Validation of EL171: 0.385351926449
[ 0.33742331 0.37654321 0.33125 0.36075949 0.3525641 ]
Random Forest Cross Validation of EL171: 0.351708023799
[ 0.39263804 0.41975309 0.36875 0.43037975 0.40384615]
Random Forest Cross Validation of EL171: 0.403073404782
EL172
[ 0.47530864 0.44654088 0.48076923 0.57692308 0.58064516]
Random Forest Cross Validation of EL172: 0.512037398292
[ 0.46296296 0.47169811 0.45512821 0.44230769 0.48387097]
Random Forest Cross Validation of EL172: 0.46319358827
[ 0.4382716 0.45283019 0.43589744 0.48076923 0.51612903]
Random Forest Cross Validation of EL172: 0.464779498508
EL295
[ 0.55833333 0.53389831 0.57264957 0.52136752 0.55172414]
Random Forest Cross Validation of EL295: 0.547594574073
[ 0.6 0.49152542 0.51282051 0.52136752 0.52586207]
Random Forest Cross Validation of EL295: 0.530315105376
[ 0.48333333 0.57627119 0.58974359 0.52136752 0.5 ]
Random Forest Cross Validation of EL295: 0.534143126177
EL395
[ 0.4516129 0.47777778 0.57303371 0.40909091 0.3908046 ]
Random Forest Cross Validation of EL395: 0.460463979132
[ 0.50537634 0.45555556 0.49438202 0.39772727 0.37931034]
Random Forest Cross Validation of EL395: 0.446470307934
[ 0.44086022 0.48888889 0.51685393 0.42045455 0.40229885]
Random Forest Cross Validation of EL395: 0.453871286511
ES356
[ 0.69230769 0.46153846 0.45454545 0.2 0.77777778]
Random Forest Cross Validation of ES356: 0.517233877234
[ 0.61538462 0.46153846 0.54545455 0.3 0.55555556]
Random Forest Cross Validation of ES356: 0.495586635587
[ 0.53846154 0.53846154 0.45454545 0.4 0.66666667]
Random Forest Cross Validation of ES356: 0.519627039627
HO201
[ 0.6 0.61818182 0.49056604 0.59183673 0.66666667]
Random Forest Cross Validation of HO201: 0.593450251456
[ 0.54545455 0.65454545 0.52830189 0.63265306 0.64583333]
Random Forest Cross Validation of HO201: 0.60135765627
[ 0.6 0.58181818 0.56603774 0.59183673 0.72916667]
Random Forest Cross Validation of HO201: 0.613771863806
HR201
[ 0.76923077 0.76923077 0.63636364 0.3 0.44444444]
Random Forest Cross Validation of HR201: 0.583853923854
[ 0.61538462 0.61538462 0.72727273 0.2 0.66666667]
Random Forest Cross Validation of HR201: 0.564941724942
[ 0.53846154 0.53846154 0.63636364 0.4 0.44444444]
Random Forest Cross Validation of HR201: 0.511546231546
LA209
[ 0.35294118 0.4 0.15384615 0.38461538 0.66666667]
Random Forest Cross Validation of LA209: 0.39161387632
[ 0.41176471 0.33333333 0.07692308 0.23076923 0.66666667]
Random Forest Cross Validation of LA209: 0.343891402715
[ 0.29411765 0.4 0.15384615 0.38461538 0.58333333]
Random Forest Cross Validation of LA209: 0.363182503771
MA211
[ 0.42372881 0.47234043 0.46153846 0.46551724 0.46320346]
Random Forest Cross Validation of MA211: 0.457265681042
[ 0.47033898 0.4212766 0.43162393 0.44396552 0.45887446]
Random Forest Cross Validation of MA211: 0.445215897307
[ 0.41949153 0.48085106 0.45299145 0.49568966 0.47186147]
Random Forest Cross Validation of MA211: 0.464177033856
MA212
[ 0.46666667 0.3697479 0.39655172 0.39473684 0.39823009]
Random Forest Cross Validation of MA212: 0.405186644113
[ 0.44166667 0.34453782 0.37931034 0.35964912 0.30088496]
Random Forest Cross Validation of MA212: 0.365209781036
[ 0.425 0.28571429 0.47413793 0.29824561 0.32743363]
Random Forest Cross Validation of MA212: 0.36210629182
MA216
[ 0.63636364 0.5 0.42857143 0.33333333 0.33333333]
Random Forest Cross Validation of MA216: 0.44632034632
[ 0.54545455 0.3 0.28571429 0.5 0.33333333]
Random Forest Cross Validation of MA216: 0.3929004329
[ 0.54545455 0.5 0.57142857 0.66666667 0.33333333]
Random Forest Cross Validation of MA216: 0.523376623377
MA332
[ 0.46363636 0.42990654 0.43925234 0.40952381 0.52380952]
Random Forest Cross Validation of MA332: 0.453225715095
[ 0.44545455 0.43925234 0.43925234 0.34285714 0.48571429]
Random Forest Cross Validation of MA332: 0.430506129385
[ 0.42727273 0.46728972 0.42990654 0.31428571 0.4952381 ]
Random Forest Cross Validation of MA332: 0.426798559696
MW313
[ 0.625 0.5 0.57142857 0.71428571 0.83333333]
Random Forest Cross Validation of MW313: 0.64880952381
[ 0.75 0.25 0.42857143 0.71428571 0.83333333]
Random Forest Cross Validation of MW313: 0.595238095238
[ 0.75 0.625 0.57142857 0.85714286 0.66666667]
Random Forest Cross Validation of MW313: 0.694047619048
MW314
[ 0.64285714 0.46153846 0.45454545 0.8 0.7 ]
Random Forest Cross Validation of MW314: 0.611788211788
[ 0.64285714 0.46153846 0.36363636 0.7 0.5 ]
Random Forest Cross Validation of MW314: 0.533606393606
[ 0.71428571 0.53846154 0.54545455 0.7 0.8 ]
Random Forest Cross Validation of MW314: 0.65964035964
NS132
[ 0.7 0.77777778 0.77777778 0.88888889 0.85714286]
Random Forest Cross Validation of NS132: 0.800317460317
[ 0.7 0.66666667 0.77777778 0.77777778 0.57142857]
Random Forest Cross Validation of NS132: 0.69873015873
[ 0.7 0.77777778 0.77777778 0.77777778 0.85714286]
Random Forest Cross Validation of NS132: 0.778095238095
PY228
[ 0.62580645 0.63870968 0.60130719 0.5751634 0.65562914]
Random Forest Cross Validation of PY228: 0.619323171268
[ 0.53548387 0.56774194 0.53594771 0.50326797 0.54304636]
Random Forest Cross Validation of PY228: 0.537097570068
[ 0.56129032 0.52903226 0.50980392 0.47712418 0.58940397]
Random Forest Cross Validation of PY228: 0.533330931746
SC123
[ 0.4 0.48275862 0.42857143 0.51851852 0.38461538]
Random Forest Cross Validation of SC123: 0.442892790479
[ 0.46666667 0.44827586 0.35714286 0.2962963 0.46153846]
Random Forest Cross Validation of SC123: 0.405984028743
[ 0.46666667 0.55172414 0.42857143 0.48148148 0.34615385]
Random Forest Cross Validation of SC123: 0.454919512161
SC135
[ 0.35403727 0.33544304 0.39490446 0.36538462 0.38961039]
Random Forest Cross Validation of SC135: 0.36787595373
[ 0.32919255 0.34810127 0.29936306 0.32051282 0.35714286]
Random Forest Cross Validation of SC135: 0.330862509477
[ 0.37267081 0.37974684 0.3566879 0.32051282 0.2987013 ]
Random Forest Cross Validation of SC135: 0.34566393204
SC173
[ 0.56666667 0.5862069 0.60714286 0.64 0.54166667]
Random Forest Cross Validation of SC173: 0.588336617406
[ 0.63333333 0.68965517 0.60714286 0.64 0.70833333]
Random Forest Cross Validation of SC173: 0.655692939245
[ 0.6 0.68965517 0.64285714 0.68 0.75 ]
Random Forest Cross Validation of SC173: 0.672502463054
SC185
[ 0.59863946 0.54794521 0.53146853 0.59574468 0.53191489]
Random Forest Cross Validation of SC185: 0.56114255344
[ 0.58503401 0.52739726 0.58041958 0.56028369 0.53900709]
Random Forest Cross Validation of SC185: 0.558428326888
[ 0.58503401 0.55479452 0.57342657 0.56737589 0.57446809]
Random Forest Cross Validation of SC185: 0.571019815842
SO201
[ 0.36363636 0.6 0.66666667 0.375 0.33333333]
Random Forest Cross Validation of SO201: 0.467727272727
[ 0.36363636 0.6 0.44444444 0.5 0.5 ]
Random Forest Cross Validation of SO201: 0.481616161616
[ 0.45454545 0.6 0.55555556 0.5 0.16666667]
Random Forest Cross Validation of SO201: 0.455353535354
ST216
[ 0.42537313 0.41353383 0.39393939 0.41538462 0.4 ]
Random Forest Cross Validation of ST216: 0.409646195648
[ 0.47014925 0.39849624 0.38636364 0.36153846 0.38461538]
Random Forest Cross Validation of ST216: 0.40023259537
[ 0.3880597 0.40601504 0.41666667 0.43076923 0.38461538]
Random Forest Cross Validation of ST216: 0.405225204228
SW111
[ 0.56521739 0.5 0.57142857 0.47368421 0.47368421]
Random Forest Cross Validation of SW111: 0.516802876757
[ 0.56521739 0.5 0.38095238 0.47368421 0.47368421]
Random Forest Cross Validation of SW111: 0.478707638662
[ 0.56521739 0.59090909 0.52380952 0.52631579 0.47368421]
Random Forest Cross Validation of SW111: 0.535987201205
SW212
[ 0.72222222 0.64705882 0.64705882 0.6875 0.85714286]
Random Forest Cross Validation of SW212: 0.712196545285
[ 0.72222222 0.70588235 0.64705882 0.625 0.78571429]
Random Forest Cross Validation of SW212: 0.697175536881
[ 0.66666667 0.70588235 0.76470588 0.625 0.64285714]
Random Forest Cross Validation of SW212: 0.681022408964
SW213
[ 0.73333333 0.76923077 0.61538462 0.90909091 0.63636364]
Random Forest Cross Validation of SW213: 0.732680652681
[ 0.66666667 0.76923077 0.69230769 0.90909091 0.72727273]
Random Forest Cross Validation of SW213: 0.752913752914
[ 0.73333333 0.69230769 0.69230769 0.63636364 0.90909091]
Random Forest Cross Validation of SW213: 0.732680652681
SW221
[ 0.72222222 0.64705882 0.6875 0.66666667 0.66666667]
Random Forest Cross Validation of SW221: 0.678022875817
[ 0.72222222 0.58823529 0.6875 0.73333333 0.66666667]
Random Forest Cross Validation of SW221: 0.679591503268
[ 0.72222222 0.58823529 0.4375 0.66666667 0.8 ]
Random Forest Cross Validation of SW221: 0.642924836601
SW335
[ 0.66666667 0.54545455 0.4 0.55555556 0.375 ]
Random Forest Cross Validation of SW335: 0.508535353535
[ 0.66666667 0.18181818 0.6 0.33333333 0.375 ]
Random Forest Cross Validation of SW335: 0.431363636364
[ 0.5 0.36363636 0.2 0.33333333 0.375 ]
Random Forest Cross Validation of SW335: 0.354393939394
SW365
[ 0.55172414 0.60714286 0.80769231 0.68 0.75 ]
Random Forest Cross Validation of SW365: 0.679311860553
[ 0.65517241 0.64285714 0.69230769 0.72 0.625 ]
Random Forest Cross Validation of SW365: 0.667067449792
[ 0.62068966 0.71428571 0.61538462 0.64 0.54166667]
Random Forest Cross Validation of SW365: 0.626405330302
SW475
[ 0.5 0.75 0.90909091 0.81818182 0.90909091]
Random Forest Cross Validation of SW475: 0.777272727273
[ 0.5 0.75 0.81818182 0.81818182 0.90909091]
Random Forest Cross Validation of SW475: 0.759090909091
[ 0.5 0.58333333 0.90909091 0.72727273 0.63636364]
Random Forest Cross Validation of SW475: 0.671212121212
SW478
[ 0.6 0.57142857 0.83333333 0.66666667 0.63636364]
Random Forest Cross Validation of SW478: 0.661558441558
[ 0.6 0.71428571 0.83333333 0.66666667 0.81818182]
Random Forest Cross Validation of SW478: 0.726493506494
[ 0.6 0.71428571 0.83333333 0.83333333 0.81818182]
Random Forest Cross Validation of SW478: 0.759826839827
TA395
[ 0.3 0.88888889 0.5 0.625 0.85714286]
Random Forest Cross Validation of TA395: 0.634206349206
[ 0.5 0.55555556 0.5 0.875 0.57142857]
Random Forest Cross Validation of TA395: 0.600396825397
[ 0.5 0.77777778 0.375 0.625 0.71428571]
Random Forest Cross Validation of TA395: 0.598412698413
TH161
[ 0.49180328 0.53038674 0.48603352 0.51396648 0.49717514]
Random Forest Cross Validation of TH161: 0.503873032053
[ 0.49726776 0.45303867 0.48044693 0.5027933 0.48587571]
Random Forest Cross Validation of TH161: 0.483884472655
[ 0.47540984 0.48066298 0.48044693 0.48044693 0.48587571]
Random Forest Cross Validation of TH161: 0.480568476091
TU100
[ 0.76388889 0.74647887 0.70422535 0.70422535 0.72857143]
Random Forest Cross Validation of TU100: 0.729477978985
[ 0.55555556 0.5915493 0.64788732 0.63380282 0.67142857]
Random Forest Cross Validation of TU100: 0.620044712721
[ 0.65277778 0.6056338 0.67605634 0.67605634 0.64285714]
Random Forest Cross Validation of TU100: 0.650676279902
TU110
[ 0.57236842 0.53642384 0.50331126 0.48666667 0.54109589]
Random Forest Cross Validation of TU110: 0.527973215494
[ 0.51973684 0.51655629 0.47019868 0.48 0.46575342]
Random Forest Cross Validation of TU110: 0.49044904673
[ 0.53289474 0.51655629 0.51655629 0.5 0.48630137]
Random Forest Cross Validation of TU110: 0.510461737897
TU120
[ 0.62732919 0.6163522 0.60759494 0.57961783 0.56410256]
Random Forest Cross Validation of TU120: 0.598999345802
[ 0.54037267 0.53459119 0.55696203 0.49681529 0.53205128]
Random Forest Cross Validation of TU120: 0.532158491954
[ 0.49068323 0.50943396 0.5443038 0.50955414 0.53205128]
Random Forest Cross Validation of TU120: 0.517205282345
TU122
[ 0.48571429 0.38235294 0.58064516 0.34482759 0.5862069 ]
Random Forest Cross Validation of TU122: 0.475949374188
[ 0.48571429 0.55882353 0.61290323 0.37931034 0.55172414]
Random Forest Cross Validation of TU122: 0.517695104738
[ 0.48571429 0.35294118 0.41935484 0.44827586 0.44827586]
Random Forest Cross Validation of TU122: 0.430912405006
TU130
[ 0.67375887 0.69285714 0.70503597 0.76086957 0.67407407]
Random Forest Cross Validation of TU130: 0.701319123724
[ 0.65248227 0.69285714 0.66906475 0.68115942 0.6 ]
Random Forest Cross Validation of TU130: 0.65911271617
[ 0.60992908 0.68571429 0.61870504 0.65942029 0.68148148]
Random Forest Cross Validation of TU130: 0.651050034207
TU154
[ 0.27083333 0.32984293 0.35263158 0.36898396 0.37096774]
Random Forest Cross Validation of TU154: 0.338651908675
[ 0.25520833 0.28795812 0.33157895 0.32620321 0.35483871]
Random Forest Cross Validation of TU154: 0.311157462824
[ 0.33854167 0.32984293 0.34210526 0.35294118 0.34946237]
Random Forest Cross Validation of TU154: 0.342578680765
In [8]:
# -*- coding: utf-8 -*-
"""
Created on Tue May 10 17:19:14 2016
@author: Methinee
"""
import pandas as pd
import numpy as np
import pickle
import xlwt
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
book = xlwt.Workbook(encoding="utf-8")
sheet1 = book.add_sheet("Precission Only Subject")
#----------------------Traning Data With Merging-----------------------
#df_file = pd.read_csv('../data/df_more20.csv',delimiter=",", skip_blank_lines = True,
# error_bad_lines=False)
df_file = pd.read_csv('../data/df_dropSub_less20_dropNaResult.csv',delimiter=",", skip_blank_lines = True,
error_bad_lines=False)
df_file = df_file.drop('Unnamed: 0',axis=1)
df_file = df_file.fillna(0)
df_file = df_file.replace(['A', 'B+', 'B', 'C+', 'C' , 'D+' , 'D' , 'F' , 'W' , 'S' , 'S#' , 'U' , 'U#'],
[8, 7, 7, 6 , 6, 5, 5, 4, 3, 2, 2, 1, 1])
count_courseId = df_file["3COURSEID"].value_counts()
more20 = count_courseId
headers=list(df_file.columns.values)
subjects = []
countSub = 0
#Create dictionary of list subjects
for sub in df_file[headers[1]]:
if sub not in subjects:
subjects.append(sub)
countSub = countSub+1
#Get subject that more 20 enrollment
count = 0
subjects.sort()
precision_rf={}
df_precision = more20.copy()
list_allsub = df_file.columns[4:]
allSubject_df = pd.DataFrame(columns=[subjects],index=[list_allsub])
top10_df = pd.DataFrame(columns=[subjects])
for subject in subjects:
#Create new Dataframe
print subject
df_sub = df_file[df_file['3COURSEID'] == subject]
df_sub = df_sub.iloc[np.random.permutation(len(df_sub))]
count_enrollment = df_sub['3COURSEID'].value_counts()
#print "Number of %s enrollment: %s"%(subject,count_enrollment)
A = df_sub.as_matrix()
X = A[:,6:117]
X = X.astype(np.int64, copy=False)
y = A[:,2]
y = y.astype(np.int64, copy=False)
#Training data
forest = RandomForestClassifier(n_estimators=10, max_depth=None,
min_samples_split=1, random_state=None, max_features=None)
clf = forest.fit(X, y)
scores = cross_val_score(clf, X, y, cv=5)
print scores
print "Random Forest Cross Validation of %s: %s"%(subject,scores.mean())
precision_rf[subject] = scores.mean()
df_precision.loc[subject]=precision_rf[subject]
sheet1.write(count, 0, subject)
sheet1.write(count,1, scores.mean())
book.save("RF_crossvalidation_dropNaResault.xls")
count = count+1
#print all subjects
#save trees to pickle file
f = "tree_drop/tree%s.pic"%subject
with open(f, 'wb') as pickleFile:
pickle.dump(clf, pickleFile, pickle.HIGHEST_PROTOCOL)
#///////////////Find Importance Feature importances with forests of trees///////////////////
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
list_grade = df_file.columns[4:]
# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %s (%f)" % (f + 1, list_grade[indices[f]], importances[indices[f]]))
allSubject_df.loc[list_grade[indices[f]],subject] = importances[indices[f]]
top10 = list_grade[indices][:10]
print str(top10)
for i in range(1,11):
top10_df.loc[i,subject] = str(top10[i-1])
print "-----------------------------------"
df_precision.plot(kind='bar')
writer = pd.ExcelWriter("feature_eachSub_dropNaResult.xlsx")
pd.DataFrame(allSubject_df).to_excel(writer,"all_feature")
pd.DataFrame(top10_df).to_excel(writer,"top10_feature")
writer.save()
AT316
[ 0.56666667 0.63333333 0.71428571 0.37037037 0.51851852]
Random Forest Cross Validation of AT316: 0.560634920635
Feature ranking:
1. feature TU122 (0.096213)
2. feature PY228 (0.080250)
3. feature TU100 (0.052582)
4. feature CS489 (0.043977)
5. feature CS231 (0.036102)
6. feature CJ321 (0.034155)
7. feature LA209 (0.033647)
8. feature EL171 (0.033572)
9. feature EL070 (0.032537)
10. feature 2SEMESTER (0.031951)
11. feature CS102 (0.031677)
12. feature CS211 (0.028227)
13. feature CJ317 (0.027441)
14. feature CS214 (0.026963)
15. feature TH161 (0.026350)
16. feature CS261 (0.025748)
17. feature SO201 (0.025270)
18. feature CS215 (0.024565)
19. feature CS115 (0.024525)
20. feature CS398 (0.023433)
21. feature SC135 (0.022794)
22. feature CS488 (0.021695)
23. feature HO201 (0.018607)
24. feature MA212 (0.016981)
25. feature HR201 (0.015745)
26. feature TA395 (0.015613)
27. feature MW314 (0.013559)
28. feature SC123 (0.013366)
29. feature TU110 (0.011371)
30. feature EL395 (0.010476)
31. feature CS251 (0.010047)
32. feature SW478 (0.009687)
33. feature SW221 (0.009673)
34. feature CS297 (0.009326)
35. feature EL295 (0.008920)
36. feature SW475 (0.007616)
37. feature SC185 (0.007465)
38. feature CS300 (0.006740)
39. feature SW212 (0.005941)
40. feature CS326 (0.004847)
41. feature NS132 (0.002913)
42. feature CS399 (0.002749)
43. feature CS223 (0.002696)
44. feature CS386 (0.002631)
45. feature MW313 (0.002461)
46. feature EL172 (0.002382)
47. feature CS377 (0.002277)
48. feature CS281 (0.002237)
49. feature AT326 (0.000000)
50. feature AT316 (0.000000)
51. feature CS296 (0.000000)
52. feature CS295 (0.000000)
53. feature CS289 (0.000000)
54. feature CS288 (0.000000)
55. feature CS286 (0.000000)
56. feature CS285 (0.000000)
57. feature CS284 (0.000000)
58. feature ES356 (0.000000)
59. feature MA211 (0.000000)
60. feature MA216 (0.000000)
61. feature SW365 (0.000000)
62. feature CS222 (0.000000)
63. feature BA291 (0.000000)
64. feature MA332 (0.000000)
65. feature CS487 (0.000000)
66. feature CS213 (0.000000)
67. feature SC173 (0.000000)
68. feature ST216 (0.000000)
69. feature CS111 (0.000000)
70. feature CS105 (0.000000)
71. feature SW111 (0.000000)
72. feature CS101 (0.000000)
73. feature SW213 (0.000000)
74. feature SW335 (0.000000)
75. feature CJ316 (0.000000)
76. feature CJ315 (0.000000)
77. feature CS459 (0.000000)
78. feature CS302 (0.000000)
79. feature CS301 (0.000000)
80. feature CS396 (0.000000)
81. feature CS456 (0.000000)
82. feature CS449 (0.000000)
83. feature CS446 (0.000000)
84. feature CS429 (0.000000)
85. feature CS427 (0.000000)
86. feature CS426 (0.000000)
87. feature CS409 (0.000000)
88. feature CS408 (0.000000)
89. feature CS407 (0.000000)
90. feature CS402 (0.000000)
91. feature CS401 (0.000000)
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
C:\Anaconda\lib\site-packages\sklearn\cross_validation.py:417: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=5.
% (min_labels, self.n_folds)), Warning)
92. feature TU120 (0.000000)
93. feature CS467 (0.000000)
94. feature CS397 (0.000000)
95. feature CS395 (0.000000)
96. feature CS457 (0.000000)
97. feature CS388 (0.000000)
98. feature CS387 (0.000000)
99. feature CS385 (0.000000)
100. feature CS374 (0.000000)
101. feature CS367 (0.000000)
102. feature CS366 (0.000000)
103. feature CS365 (0.000000)
104. feature CS356 (0.000000)
105. feature CS348 (0.000000)
106. feature CS342 (0.000000)
107. feature CS341 (0.000000)
108. feature CS486 (0.000000)
109. feature CS314 (0.000000)
110. feature CS311 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'TU122', u'PY228', u'TU100', u'CS489', u'CS231', u'CJ321', u'LA209',
u'EL171', u'EL070', u'2SEMESTER'],
dtype='object')
-----------------------------------
AT326
[ 0.56521739 0.64444444 0.65909091 0.60465116 0.60465116]
Random Forest Cross Validation of AT326: 0.615611014084
Feature ranking:
1. feature MW314 (0.110502)
2. feature CJ321 (0.089673)
3. feature EL070 (0.073435)
4. feature PY228 (0.070538)
5. feature TU122 (0.052509)
6. feature TA395 (0.051082)
7. feature HO201 (0.041959)
8. feature TH161 (0.040549)
9. feature HR201 (0.038000)
10. feature TU100 (0.035714)
11. feature CS101 (0.030097)
12. feature CS102 (0.029095)
13. feature CS489 (0.027468)
14. feature CS488 (0.026728)
15. feature TU120 (0.024573)
16. feature CJ317 (0.024540)
17. feature TU110 (0.024533)
18. feature EL395 (0.023315)
19. feature SW478 (0.017149)
20. feature EL171 (0.016644)
21. feature SC135 (0.014980)
22. feature SO201 (0.014620)
23. feature CS289 (0.011984)
24. feature LA209 (0.011316)
25. feature SC123 (0.008858)
26. feature CS214 (0.008368)
27. feature CS251 (0.007822)
28. feature CS231 (0.007022)
29. feature NS132 (0.006914)
30. feature 2SEMESTER (0.006577)
31. feature SC173 (0.005876)
32. feature MA212 (0.005654)
33. feature CS115 (0.005037)
34. feature SW475 (0.004920)
35. feature 1ACADYEAR (0.004797)
36. feature MW313 (0.004493)
37. feature CS261 (0.003926)
38. feature SW221 (0.003179)
39. feature CS366 (0.002729)
40. feature CS211 (0.002526)
41. feature CS223 (0.001969)
42. feature EL172 (0.001873)
43. feature CS297 (0.001591)
44. feature SW213 (0.001516)
45. feature CS215 (0.001293)
46. feature SW212 (0.001250)
47. feature CS288 (0.000810)
48. feature CS296 (0.000000)
49. feature CS295 (0.000000)
50. feature CS300 (0.000000)
51. feature MA332 (0.000000)
52. feature CS286 (0.000000)
53. feature CS301 (0.000000)
54. feature CS285 (0.000000)
55. feature CS284 (0.000000)
56. feature CS281 (0.000000)
57. feature CS302 (0.000000)
58. feature CS222 (0.000000)
59. feature SC185 (0.000000)
60. feature CS314 (0.000000)
61. feature ST216 (0.000000)
62. feature CS213 (0.000000)
63. feature SW111 (0.000000)
64. feature CS111 (0.000000)
65. feature CS105 (0.000000)
66. feature SW335 (0.000000)
67. feature CJ316 (0.000000)
68. feature CJ315 (0.000000)
69. feature BA291 (0.000000)
70. feature AT326 (0.000000)
71. feature AT316 (0.000000)
72. feature SW365 (0.000000)
73. feature CS311 (0.000000)
74. feature CS326 (0.000000)
75. feature EL295 (0.000000)
76. feature CS402 (0.000000)
77. feature CS408 (0.000000)
78. feature CS409 (0.000000)
79. feature CS426 (0.000000)
80. feature CS427 (0.000000)
81. feature CS429 (0.000000)
82. feature CS446 (0.000000)
83. feature CS449 (0.000000)
84. feature CS456 (0.000000)
85. feature CS457 (0.000000)
86. feature CS459 (0.000000)
87. feature CS467 (0.000000)
88. feature CS486 (0.000000)
89. feature CS487 (0.000000)
90. feature MA211 (0.000000)
91. feature ES356 (0.000000)
92. feature CS407 (0.000000)
93. feature CS401 (0.000000)
94. feature CS341 (0.000000)
95. feature MA216 (0.000000)
96. feature CS342 (0.000000)
97. feature CS348 (0.000000)
98. feature CS356 (0.000000)
99. feature CS365 (0.000000)
100. feature CS367 (0.000000)
101. feature CS374 (0.000000)
102. feature CS377 (0.000000)
103. feature CS385 (0.000000)
104. feature CS386 (0.000000)
105. feature CS387 (0.000000)
106. feature CS388 (0.000000)
107. feature CS395 (0.000000)
108. feature CS396 (0.000000)
109. feature CS397 (0.000000)
110. feature CS398 (0.000000)
111. feature CS399 (0.000000)
Index([u'MW314', u'CJ321', u'EL070', u'PY228', u'TU122', u'TA395', u'HO201',
u'TH161', u'HR201', u'TU100'],
dtype='object')
-----------------------------------
BA291
[ 0.07142857 0.5 0.54545455 0.36363636 0.3 ]
Random Forest Cross Validation of BA291: 0.356103896104
Feature ranking:
1. feature TU100 (0.062568)
2. feature MA212 (0.059344)
3. feature CS251 (0.052409)
4. feature PY228 (0.052310)
5. feature CS223 (0.048257)
6. feature HO201 (0.047855)
7. feature SC135 (0.047242)
8. feature CS261 (0.045514)
9. feature SC185 (0.045126)
10. feature HR201 (0.039542)
11. feature CS215 (0.038636)
12. feature CS211 (0.036534)
13. feature LA209 (0.034223)
14. feature CS297 (0.033491)
15. feature SW478 (0.029271)
16. feature CJ321 (0.026352)
17. feature EL171 (0.021704)
18. feature CS289 (0.021262)
19. feature TA395 (0.020334)
20. feature EL172 (0.017649)
21. feature CJ317 (0.017311)
22. feature TU122 (0.016554)
23. feature TH161 (0.014084)
24. feature CS115 (0.013643)
25. feature CS102 (0.012450)
26. feature CS487 (0.011729)
27. feature CS387 (0.011544)
28. feature CS459 (0.011374)
29. feature CS214 (0.010079)
30. feature CS285 (0.009923)
31. feature 2SEMESTER (0.008393)
32. feature 1ACADYEAR (0.007825)
33. feature TU120 (0.007188)
34. feature SW475 (0.007010)
35. feature NS132 (0.006386)
36. feature TU110 (0.006128)
37. feature CS231 (0.004753)
38. feature CS374 (0.004708)
39. feature ST216 (0.004694)
40. feature CS311 (0.004487)
41. feature CS467 (0.004214)
42. feature SW212 (0.004113)
43. feature CS488 (0.003901)
44. feature CS288 (0.003853)
45. feature CS314 (0.003702)
46. feature CS407 (0.003468)
47. feature CS489 (0.003468)
48. feature CS348 (0.003395)
49. feature MW314 (0.000000)
50. feature SC173 (0.000000)
51. feature CS301 (0.000000)
52. feature CS281 (0.000000)
53. feature CS286 (0.000000)
54. feature CS284 (0.000000)
55. feature SC123 (0.000000)
56. feature SO201 (0.000000)
57. feature CS296 (0.000000)
58. feature CS300 (0.000000)
59. feature CS295 (0.000000)
60. feature EL395 (0.000000)
61. feature CS222 (0.000000)
62. feature CS302 (0.000000)
63. feature CS213 (0.000000)
64. feature SW213 (0.000000)
65. feature SW221 (0.000000)
66. feature CS111 (0.000000)
67. feature CS105 (0.000000)
68. feature SW335 (0.000000)
69. feature CS101 (0.000000)
70. feature SW365 (0.000000)
71. feature CJ316 (0.000000)
72. feature CJ315 (0.000000)
73. feature BA291 (0.000000)
74. feature AT326 (0.000000)
75. feature AT316 (0.000000)
76. feature SW111 (0.000000)
77. feature CS342 (0.000000)
78. feature CS326 (0.000000)
79. feature CS456 (0.000000)
80. feature CS409 (0.000000)
81. feature CS426 (0.000000)
82. feature CS427 (0.000000)
83. feature CS429 (0.000000)
84. feature CS446 (0.000000)
85. feature CS449 (0.000000)
86. feature CS457 (0.000000)
87. feature CS341 (0.000000)
88. feature MA332 (0.000000)
89. feature CS486 (0.000000)
90. feature MA216 (0.000000)
91. feature EL070 (0.000000)
92. feature MA211 (0.000000)
93. feature ES356 (0.000000)
94. feature CS408 (0.000000)
95. feature CS402 (0.000000)
96. feature CS401 (0.000000)
97. feature CS398 (0.000000)
98. feature CS397 (0.000000)
99. feature CS396 (0.000000)
100. feature CS395 (0.000000)
101. feature CS388 (0.000000)
102. feature MW313 (0.000000)
103. feature CS386 (0.000000)
104. feature CS385 (0.000000)
105. feature CS377 (0.000000)
106. feature CS367 (0.000000)
107. feature CS366 (0.000000)
108. feature CS365 (0.000000)
109. feature CS356 (0.000000)
110. feature EL295 (0.000000)
111. feature CS399 (0.000000)
Index([u'TU100', u'MA212', u'CS251', u'PY228', u'CS223', u'HO201', u'SC135',
u'CS261', u'SC185', u'HR201'],
dtype='object')
-----------------------------------
CJ315
[ 0.57142857 0.4 0.8 0.6 0.75 ]
Random Forest Cross Validation of CJ315: 0.624285714286
Feature ranking:
1. feature TU122 (0.186406)
2. feature CS288 (0.111877)
3. feature SC185 (0.103565)
4. feature CS489 (0.100866)
5. feature CS102 (0.075587)
6. feature CS289 (0.044992)
7. feature HR201 (0.043784)
8. feature CJ321 (0.043364)
9. feature MA332 (0.040556)
10. feature SW212 (0.036111)
11. feature CS231 (0.033681)
12. feature CS211 (0.032924)
13. feature SW478 (0.030930)
14. feature CS261 (0.024413)
15. feature NS132 (0.019797)
16. feature EL172 (0.019289)
17. feature SW111 (0.015703)
18. feature CS215 (0.014054)
19. feature CS286 (0.011726)
20. feature MW314 (0.005689)
21. feature TU100 (0.004686)
22. feature CS301 (0.000000)
23. feature CS326 (0.000000)
24. feature CS300 (0.000000)
25. feature CS297 (0.000000)
26. feature CS302 (0.000000)
27. feature CS311 (0.000000)
28. feature CS314 (0.000000)
29. feature CJ317 (0.000000)
30. feature CS341 (0.000000)
31. feature CS342 (0.000000)
32. feature CS295 (0.000000)
33. feature CS348 (0.000000)
34. feature CS356 (0.000000)
35. feature CS365 (0.000000)
36. feature CS366 (0.000000)
37. feature CS367 (0.000000)
38. feature CS374 (0.000000)
39. feature CS377 (0.000000)
40. feature CS385 (0.000000)
41. feature CS296 (0.000000)
42. feature CS285 (0.000000)
43. feature 2SEMESTER (0.000000)
44. feature CS214 (0.000000)
45. feature CS101 (0.000000)
46. feature CJ315 (0.000000)
47. feature BA291 (0.000000)
48. feature CS105 (0.000000)
49. feature CS111 (0.000000)
50. feature CS115 (0.000000)
51. feature CS213 (0.000000)
52. feature CS222 (0.000000)
53. feature AT316 (0.000000)
54. feature CS223 (0.000000)
55. feature AT326 (0.000000)
56. feature CS387 (0.000000)
57. feature CS251 (0.000000)
58. feature CS281 (0.000000)
59. feature CS284 (0.000000)
60. feature CJ316 (0.000000)
61. feature CS386 (0.000000)
62. feature CS399 (0.000000)
63. feature CS388 (0.000000)
64. feature EL295 (0.000000)
65. feature ES356 (0.000000)
66. feature HO201 (0.000000)
67. feature LA209 (0.000000)
68. feature MA211 (0.000000)
69. feature MA212 (0.000000)
70. feature MA216 (0.000000)
71. feature MW313 (0.000000)
72. feature PY228 (0.000000)
73. feature SC123 (0.000000)
74. feature SC135 (0.000000)
75. feature SC173 (0.000000)
76. feature SO201 (0.000000)
77. feature ST216 (0.000000)
78. feature SW213 (0.000000)
79. feature SW221 (0.000000)
80. feature SW335 (0.000000)
81. feature SW365 (0.000000)
82. feature SW475 (0.000000)
83. feature TA395 (0.000000)
84. feature TH161 (0.000000)
85. feature TU110 (0.000000)
86. feature EL395 (0.000000)
87. feature EL171 (0.000000)
88. feature CS395 (0.000000)
89. feature EL070 (0.000000)
90. feature CS396 (0.000000)
91. feature CS397 (0.000000)
92. feature CS398 (0.000000)
93. feature TU120 (0.000000)
94. feature CS401 (0.000000)
95. feature CS402 (0.000000)
96. feature CS407 (0.000000)
97. feature CS408 (0.000000)
98. feature CS409 (0.000000)
99. feature CS426 (0.000000)
100. feature CS427 (0.000000)
101. feature CS429 (0.000000)
102. feature CS446 (0.000000)
103. feature CS449 (0.000000)
104. feature CS456 (0.000000)
105. feature CS457 (0.000000)
106. feature CS459 (0.000000)
107. feature CS467 (0.000000)
108. feature CS486 (0.000000)
109. feature CS487 (0.000000)
110. feature CS488 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'TU122', u'CS288', u'SC185', u'CS489', u'CS102', u'CS289', u'HR201',
u'CJ321', u'MA332', u'SW212'],
dtype='object')
-----------------------------------
CJ316
[ 0.66666667 0.625 0.75 0.5 0.6 ]
Random Forest Cross Validation of CJ316: 0.628333333333
Feature ranking:
1. feature EL070 (0.069958)
2. feature SW365 (0.066464)
3. feature MA332 (0.060386)
4. feature EL395 (0.053038)
5. feature SW478 (0.046956)
6. feature TU100 (0.045804)
7. feature HR201 (0.042353)
8. feature CS488 (0.042277)
9. feature SW221 (0.042151)
10. feature CJ321 (0.041181)
11. feature SC185 (0.040833)
12. feature CS426 (0.037962)
13. feature SC135 (0.037505)
14. feature CS302 (0.032393)
15. feature LA209 (0.031034)
16. feature CS399 (0.028241)
17. feature TU120 (0.027553)
18. feature PY228 (0.026987)
19. feature CS102 (0.025670)
20. feature MA212 (0.023732)
21. feature CS211 (0.022827)
22. feature CS301 (0.020930)
23. feature EL295 (0.016170)
24. feature TH161 (0.015909)
25. feature CS489 (0.013557)
26. feature CS223 (0.013191)
27. feature SW111 (0.012064)
28. feature TU122 (0.010588)
29. feature CS285 (0.010213)
30. feature CS398 (0.008481)
31. feature CJ315 (0.008295)
32. feature CS286 (0.007295)
33. feature CS348 (0.006844)
34. feature CS231 (0.006844)
35. feature MW314 (0.004311)
36. feature CS311 (0.000000)
37. feature CS288 (0.000000)
38. feature CS261 (0.000000)
39. feature CS281 (0.000000)
40. feature CS284 (0.000000)
41. feature CS341 (0.000000)
42. feature CS326 (0.000000)
43. feature CS314 (0.000000)
44. feature CS289 (0.000000)
45. feature CS295 (0.000000)
46. feature CS296 (0.000000)
47. feature CS251 (0.000000)
48. feature CS300 (0.000000)
49. feature SW213 (0.000000)
50. feature SW212 (0.000000)
51. feature CS297 (0.000000)
52. feature MA211 (0.000000)
53. feature CS342 (0.000000)
54. feature CS101 (0.000000)
55. feature 2SEMESTER (0.000000)
56. feature AT316 (0.000000)
57. feature AT326 (0.000000)
58. feature BA291 (0.000000)
59. feature CJ316 (0.000000)
60. feature CJ317 (0.000000)
61. feature TU110 (0.000000)
62. feature TA395 (0.000000)
63. feature CS222 (0.000000)
64. feature CS105 (0.000000)
65. feature CS111 (0.000000)
66. feature CS115 (0.000000)
67. feature SW475 (0.000000)
68. feature CS213 (0.000000)
69. feature CS214 (0.000000)
70. feature CS215 (0.000000)
71. feature SW335 (0.000000)
72. feature CS356 (0.000000)
73. feature MA216 (0.000000)
74. feature CS487 (0.000000)
75. feature CS446 (0.000000)
76. feature CS449 (0.000000)
77. feature CS456 (0.000000)
78. feature CS457 (0.000000)
79. feature CS459 (0.000000)
80. feature CS467 (0.000000)
81. feature CS486 (0.000000)
82. feature SC173 (0.000000)
83. feature CS427 (0.000000)
84. feature SC123 (0.000000)
85. feature NS132 (0.000000)
86. feature EL171 (0.000000)
87. feature EL172 (0.000000)
88. feature MW313 (0.000000)
89. feature ES356 (0.000000)
90. feature HO201 (0.000000)
91. feature CS429 (0.000000)
92. feature SO201 (0.000000)
93. feature CS365 (0.000000)
94. feature CS388 (0.000000)
95. feature CS366 (0.000000)
96. feature CS367 (0.000000)
97. feature CS374 (0.000000)
98. feature CS377 (0.000000)
99. feature CS385 (0.000000)
100. feature CS386 (0.000000)
101. feature CS387 (0.000000)
102. feature CS395 (0.000000)
103. feature CS409 (0.000000)
104. feature CS396 (0.000000)
105. feature CS397 (0.000000)
106. feature ST216 (0.000000)
107. feature CS401 (0.000000)
108. feature CS402 (0.000000)
109. feature CS407 (0.000000)
110. feature CS408 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'EL070', u'SW365', u'MA332', u'EL395', u'SW478', u'TU100', u'HR201',
u'CS488', u'SW221', u'CJ321'],
dtype='object')
-----------------------------------
CJ317
[ 0.6 0.57142857 1. 0.57142857 0.83333333]
Random Forest Cross Validation of CJ317: 0.715238095238
Feature ranking:
1. feature CS102 (0.161649)
2. feature SW335 (0.107445)
3. feature SO201 (0.103309)
4. feature MA216 (0.099479)
5. feature CS489 (0.075025)
6. feature SW478 (0.073204)
7. feature CJ321 (0.048533)
8. feature LA209 (0.043820)
9. feature EL070 (0.030622)
10. feature TU120 (0.030559)
11. feature CS459 (0.028591)
12. feature CS223 (0.026352)
13. feature CS288 (0.024013)
14. feature CS301 (0.023287)
15. feature TU100 (0.018123)
16. feature MW314 (0.017494)
17. feature CS487 (0.015721)
18. feature HR201 (0.014719)
19. feature SC135 (0.012350)
20. feature CS302 (0.009308)
21. feature CS115 (0.009120)
22. feature EL171 (0.007895)
23. feature CS261 (0.005153)
24. feature TU122 (0.004987)
25. feature CS284 (0.004920)
26. feature CS211 (0.004322)
27. feature CS311 (0.000000)
28. feature CS289 (0.000000)
29. feature CS374 (0.000000)
30. feature CS295 (0.000000)
31. feature CS367 (0.000000)
32. feature CS366 (0.000000)
33. feature CS365 (0.000000)
34. feature CS296 (0.000000)
35. feature CS356 (0.000000)
36. feature CS297 (0.000000)
37. feature CS300 (0.000000)
38. feature CS348 (0.000000)
39. feature CS342 (0.000000)
40. feature CS341 (0.000000)
41. feature CS326 (0.000000)
42. feature CS314 (0.000000)
43. feature CS251 (0.000000)
44. feature CS286 (0.000000)
45. feature CS285 (0.000000)
46. feature 2SEMESTER (0.000000)
47. feature AT316 (0.000000)
48. feature AT326 (0.000000)
49. feature BA291 (0.000000)
50. feature CJ315 (0.000000)
51. feature CJ316 (0.000000)
52. feature CJ317 (0.000000)
53. feature CS101 (0.000000)
54. feature CS105 (0.000000)
55. feature CS111 (0.000000)
56. feature CS213 (0.000000)
57. feature CS214 (0.000000)
58. feature CS215 (0.000000)
59. feature CS222 (0.000000)
60. feature CS231 (0.000000)
61. feature CS385 (0.000000)
62. feature CS281 (0.000000)
63. feature CS377 (0.000000)
64. feature CS399 (0.000000)
65. feature CS386 (0.000000)
66. feature SC185 (0.000000)
67. feature HO201 (0.000000)
68. feature MA211 (0.000000)
69. feature MA212 (0.000000)
70. feature MA332 (0.000000)
71. feature MW313 (0.000000)
72. feature NS132 (0.000000)
73. feature PY228 (0.000000)
74. feature SC123 (0.000000)
75. feature SC173 (0.000000)
76. feature ST216 (0.000000)
77. feature EL395 (0.000000)
78. feature SW111 (0.000000)
79. feature SW212 (0.000000)
80. feature SW213 (0.000000)
81. feature SW221 (0.000000)
82. feature SW365 (0.000000)
83. feature SW475 (0.000000)
84. feature TA395 (0.000000)
85. feature TH161 (0.000000)
86. feature TU110 (0.000000)
87. feature ES356 (0.000000)
88. feature EL295 (0.000000)
89. feature CS387 (0.000000)
90. feature CS409 (0.000000)
91. feature CS388 (0.000000)
92. feature CS395 (0.000000)
93. feature CS396 (0.000000)
94. feature CS397 (0.000000)
95. feature CS398 (0.000000)
96. feature CS401 (0.000000)
97. feature CS402 (0.000000)
98. feature CS407 (0.000000)
99. feature CS408 (0.000000)
100. feature CS426 (0.000000)
101. feature EL172 (0.000000)
102. feature CS427 (0.000000)
103. feature CS429 (0.000000)
104. feature CS446 (0.000000)
105. feature CS449 (0.000000)
106. feature CS456 (0.000000)
107. feature CS457 (0.000000)
108. feature CS467 (0.000000)
109. feature CS486 (0.000000)
110. feature CS488 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'CS102', u'SW335', u'SO201', u'MA216', u'CS489', u'SW478', u'CJ321',
u'LA209', u'EL070', u'TU120'],
dtype='object')
-----------------------------------
CJ321
[ 0.5 0.625 0.75 0.625 0.71428571]
Random Forest Cross Validation of CJ321: 0.642857142857
Feature ranking:
1. feature MW314 (0.166345)
2. feature EL070 (0.095803)
3. feature TU122 (0.088441)
4. feature TU120 (0.073921)
5. feature CS231 (0.055491)
6. feature CS356 (0.046621)
7. feature CS102 (0.042377)
8. feature TU100 (0.040135)
9. feature CJ321 (0.039430)
10. feature PY228 (0.038617)
11. feature TH161 (0.033357)
12. feature SW212 (0.030850)
13. feature SW221 (0.029783)
14. feature SW365 (0.027980)
15. feature EL295 (0.026000)
16. feature CS214 (0.025743)
17. feature CS366 (0.023841)
18. feature CS285 (0.022409)
19. feature CS115 (0.021449)
20. feature CS223 (0.017405)
21. feature SC135 (0.014211)
22. feature CS314 (0.013733)
23. feature MA212 (0.012412)
24. feature SO201 (0.011682)
25. feature EL171 (0.001964)
26. feature CS302 (0.000000)
27. feature CS374 (0.000000)
28. feature CS296 (0.000000)
29. feature CS297 (0.000000)
30. feature CS367 (0.000000)
31. feature CS300 (0.000000)
32. feature CS301 (0.000000)
33. feature 2SEMESTER (0.000000)
34. feature CS311 (0.000000)
35. feature CS326 (0.000000)
36. feature CS365 (0.000000)
37. feature CS341 (0.000000)
38. feature CS342 (0.000000)
39. feature CS289 (0.000000)
40. feature CS348 (0.000000)
41. feature CS295 (0.000000)
42. feature AT316 (0.000000)
43. feature CS288 (0.000000)
44. feature CS286 (0.000000)
45. feature BA291 (0.000000)
46. feature CJ315 (0.000000)
47. feature CJ316 (0.000000)
48. feature CJ317 (0.000000)
49. feature CS101 (0.000000)
50. feature CS105 (0.000000)
51. feature CS111 (0.000000)
52. feature CS211 (0.000000)
53. feature CS213 (0.000000)
54. feature CS215 (0.000000)
55. feature CS222 (0.000000)
56. feature CS385 (0.000000)
57. feature CS251 (0.000000)
58. feature AT326 (0.000000)
59. feature CS261 (0.000000)
60. feature CS281 (0.000000)
61. feature CS284 (0.000000)
62. feature CS377 (0.000000)
63. feature CS399 (0.000000)
64. feature CS386 (0.000000)
65. feature CS387 (0.000000)
66. feature EL395 (0.000000)
67. feature ES356 (0.000000)
68. feature HO201 (0.000000)
69. feature HR201 (0.000000)
70. feature LA209 (0.000000)
71. feature MA211 (0.000000)
72. feature MA216 (0.000000)
73. feature MA332 (0.000000)
74. feature MW313 (0.000000)
75. feature NS132 (0.000000)
76. feature SC123 (0.000000)
77. feature SC173 (0.000000)
78. feature SC185 (0.000000)
79. feature ST216 (0.000000)
80. feature SW111 (0.000000)
81. feature SW213 (0.000000)
82. feature SW335 (0.000000)
83. feature SW475 (0.000000)
84. feature SW478 (0.000000)
85. feature TA395 (0.000000)
86. feature TU110 (0.000000)
87. feature EL172 (0.000000)
88. feature CS489 (0.000000)
89. feature CS488 (0.000000)
90. feature CS409 (0.000000)
91. feature CS388 (0.000000)
92. feature CS395 (0.000000)
93. feature CS396 (0.000000)
94. feature CS397 (0.000000)
95. feature CS398 (0.000000)
96. feature CS401 (0.000000)
97. feature CS402 (0.000000)
98. feature CS407 (0.000000)
99. feature CS408 (0.000000)
100. feature CS426 (0.000000)
101. feature CS487 (0.000000)
102. feature CS427 (0.000000)
103. feature CS429 (0.000000)
104. feature CS446 (0.000000)
105. feature CS449 (0.000000)
106. feature CS456 (0.000000)
107. feature CS457 (0.000000)
108. feature CS459 (0.000000)
109. feature CS467 (0.000000)
110. feature CS486 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'MW314', u'EL070', u'TU122', u'TU120', u'CS231', u'CS356', u'CS102',
u'TU100', u'CJ321', u'PY228'],
dtype='object')
-----------------------------------
CS101
[ 0.52083333 0.53157895 0.53968254 0.52910053 0.54255319]
Random Forest Cross Validation of CS101: 0.532749708195
Feature ranking:
1. feature SO201 (0.065763)
2. feature SW478 (0.063564)
3. feature MA212 (0.050417)
4. feature TH161 (0.045900)
5. feature CS489 (0.041033)
6. feature CJ321 (0.037261)
7. feature SC185 (0.037036)
8. feature CS102 (0.036525)
9. feature CS261 (0.034758)
10. feature CS223 (0.030266)
11. feature MW314 (0.028958)
12. feature CS211 (0.028459)
13. feature SC135 (0.027335)
14. feature SC123 (0.025694)
15. feature EL070 (0.023460)
16. feature CS115 (0.023215)
17. feature CS215 (0.021921)
18. feature CS367 (0.020558)
19. feature EL295 (0.019649)
20. feature EL171 (0.019053)
21. feature NS132 (0.016774)
22. feature CS284 (0.016239)
23. feature ST216 (0.015850)
24. feature CJ315 (0.015097)
25. feature 2SEMESTER (0.014968)
26. feature TU122 (0.014288)
27. feature TU100 (0.014260)
28. feature PY228 (0.014236)
29. feature CS488 (0.013854)
30. feature MA216 (0.013063)
31. feature CS366 (0.012865)
32. feature CS399 (0.012577)
33. feature MA332 (0.012170)
34. feature CS251 (0.012004)
35. feature CS374 (0.011929)
36. feature CJ317 (0.010298)
37. feature CS101 (0.008769)
38. feature SW111 (0.008386)
39. feature SW212 (0.008002)
40. feature CS231 (0.007979)
41. feature TU110 (0.007951)
42. feature TU120 (0.007410)
43. feature CS289 (0.006942)
44. feature EL395 (0.006758)
45. feature CS288 (0.006249)
46. feature LA209 (0.005907)
47. feature CS348 (0.005881)
48. feature CS341 (0.005292)
49. feature CS356 (0.005007)
50. feature CS314 (0.004330)
51. feature HR201 (0.003839)
52. feature SW475 (0.000000)
53. feature SW365 (0.000000)
54. feature CS300 (0.000000)
55. feature CS297 (0.000000)
56. feature CS296 (0.000000)
57. feature CS295 (0.000000)
58. feature AT316 (0.000000)
59. feature CS286 (0.000000)
60. feature CS285 (0.000000)
61. feature SC173 (0.000000)
62. feature CS281 (0.000000)
63. feature AT326 (0.000000)
64. feature BA291 (0.000000)
65. feature TA395 (0.000000)
66. feature CJ316 (0.000000)
67. feature CS302 (0.000000)
68. feature CS222 (0.000000)
69. feature SW213 (0.000000)
70. feature CS214 (0.000000)
71. feature CS213 (0.000000)
72. feature SW221 (0.000000)
73. feature SW335 (0.000000)
74. feature CS111 (0.000000)
75. feature CS105 (0.000000)
76. feature CS301 (0.000000)
77. feature CS342 (0.000000)
78. feature CS311 (0.000000)
79. feature CS457 (0.000000)
80. feature CS426 (0.000000)
81. feature CS427 (0.000000)
82. feature CS429 (0.000000)
83. feature CS446 (0.000000)
84. feature CS449 (0.000000)
85. feature CS456 (0.000000)
86. feature CS459 (0.000000)
87. feature CS326 (0.000000)
88. feature CS467 (0.000000)
89. feature CS486 (0.000000)
90. feature CS487 (0.000000)
91. feature MA211 (0.000000)
92. feature HO201 (0.000000)
93. feature EL172 (0.000000)
94. feature CS409 (0.000000)
95. feature CS408 (0.000000)
96. feature CS407 (0.000000)
97. feature CS402 (0.000000)
98. feature CS401 (0.000000)
99. feature CS398 (0.000000)
100. feature CS397 (0.000000)
101. feature CS396 (0.000000)
102. feature CS395 (0.000000)
103. feature CS388 (0.000000)
104. feature CS387 (0.000000)
105. feature CS386 (0.000000)
106. feature CS385 (0.000000)
107. feature CS377 (0.000000)
108. feature MW313 (0.000000)
109. feature CS365 (0.000000)
110. feature ES356 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'SO201', u'SW478', u'MA212', u'TH161', u'CS489', u'CJ321', u'SC185',
u'CS102', u'CS261', u'CS223'],
dtype='object')
-----------------------------------
CS102
[ 0.42051282 0.41025641 0.42268041 0.41145833 0.43157895]
Random Forest Cross Validation of CS102: 0.419297384768
Feature ranking:
1. feature CS366 (0.116497)
2. feature TU100 (0.088007)
3. feature CJ317 (0.060020)
4. feature SW478 (0.051532)
5. feature PY228 (0.046896)
6. feature CS261 (0.046832)
7. feature CS284 (0.045061)
8. feature CS102 (0.042352)
9. feature HR201 (0.038989)
10. feature CS314 (0.033837)
11. feature MW314 (0.031268)
12. feature TU122 (0.030152)
13. feature SC185 (0.022196)
14. feature CS467 (0.021952)
15. feature AT326 (0.019586)
16. feature MA212 (0.019550)
17. feature CS297 (0.019515)
18. feature EL172 (0.017759)
19. feature LA209 (0.017461)
20. feature SO201 (0.015298)
21. feature SC135 (0.014828)
22. feature CS326 (0.014762)
23. feature EL171 (0.014658)
24. feature EL070 (0.014563)
25. feature TH161 (0.012864)
26. feature CS251 (0.012672)
27. feature MA332 (0.011905)
28. feature CS348 (0.010830)
29. feature CS301 (0.010823)
30. feature CS489 (0.009857)
31. feature SW335 (0.007513)
32. feature CS211 (0.007256)
33. feature CS488 (0.007169)
34. feature 2SEMESTER (0.006848)
35. feature ST216 (0.005948)
36. feature CS302 (0.005545)
37. feature CS286 (0.004951)
38. feature CS402 (0.004588)
39. feature CJ315 (0.004515)
40. feature CS387 (0.004182)
41. feature TA395 (0.004051)
42. feature TU110 (0.003979)
43. feature 1ACADYEAR (0.003809)
44. feature CS374 (0.003703)
45. feature CS285 (0.003214)
46. feature TU120 (0.003186)
47. feature CS341 (0.003047)
48. feature CS459 (0.001989)
49. feature CS223 (0.001984)
50. feature CS457 (0.000000)
51. feature SC123 (0.000000)
52. feature CS222 (0.000000)
53. feature CS231 (0.000000)
54. feature SC173 (0.000000)
55. feature CS281 (0.000000)
56. feature CS214 (0.000000)
57. feature NS132 (0.000000)
58. feature MW313 (0.000000)
59. feature CS288 (0.000000)
60. feature CS289 (0.000000)
61. feature CS295 (0.000000)
62. feature CS215 (0.000000)
63. feature CS213 (0.000000)
64. feature MA216 (0.000000)
65. feature SW111 (0.000000)
66. feature CS115 (0.000000)
67. feature CS111 (0.000000)
68. feature CS105 (0.000000)
69. feature SW212 (0.000000)
70. feature CS101 (0.000000)
71. feature CJ321 (0.000000)
72. feature SW213 (0.000000)
73. feature CJ316 (0.000000)
74. feature SW221 (0.000000)
75. feature BA291 (0.000000)
76. feature SW365 (0.000000)
77. feature AT316 (0.000000)
78. feature SW475 (0.000000)
79. feature CS296 (0.000000)
80. feature MA211 (0.000000)
81. feature CS300 (0.000000)
82. feature CS388 (0.000000)
83. feature CS449 (0.000000)
84. feature CS446 (0.000000)
85. feature CS429 (0.000000)
86. feature CS427 (0.000000)
87. feature CS426 (0.000000)
88. feature CS409 (0.000000)
89. feature CS408 (0.000000)
90. feature CS407 (0.000000)
91. feature CS401 (0.000000)
92. feature CS398 (0.000000)
93. feature CS397 (0.000000)
94. feature CS396 (0.000000)
95. feature CS395 (0.000000)
96. feature CS486 (0.000000)
97. feature CS456 (0.000000)
98. feature CS386 (0.000000)
99. feature CS385 (0.000000)
100. feature CS377 (0.000000)
101. feature CS367 (0.000000)
102. feature CS487 (0.000000)
103. feature CS365 (0.000000)
104. feature CS356 (0.000000)
105. feature EL295 (0.000000)
106. feature CS342 (0.000000)
107. feature EL395 (0.000000)
108. feature ES356 (0.000000)
109. feature CS311 (0.000000)
110. feature HO201 (0.000000)
111. feature CS399 (0.000000)
Index([u'CS366', u'TU100', u'CJ317', u'SW478', u'PY228', u'CS261', u'CS284',
u'CS102', u'HR201', u'CS314'],
dtype='object')
-----------------------------------
CS105
[ 0.30645161 0.31147541 0.3 0.31034483 0.31578947]
Random Forest Cross Validation of CS105: 0.308812264802
Feature ranking:
1. feature TU122 (0.000000)
2. feature CS348 (0.000000)
3. feature CS295 (0.000000)
4. feature CS296 (0.000000)
5. feature CS297 (0.000000)
6. feature CS300 (0.000000)
7. feature CS301 (0.000000)
8. feature CS302 (0.000000)
9. feature CS311 (0.000000)
10. feature CS314 (0.000000)
11. feature CS326 (0.000000)
12. feature CS341 (0.000000)
13. feature CS342 (0.000000)
14. feature CS356 (0.000000)
15. feature CS288 (0.000000)
16. feature CS365 (0.000000)
17. feature CS366 (0.000000)
18. feature CS367 (0.000000)
19. feature CS374 (0.000000)
20. feature CS377 (0.000000)
21. feature CS385 (0.000000)
22. feature CS386 (0.000000)
23. feature CS387 (0.000000)
24. feature CS388 (0.000000)
25. feature CS395 (0.000000)
26. feature CS396 (0.000000)
27. feature CS289 (0.000000)
28. feature CS286 (0.000000)
29. feature CS398 (0.000000)
30. feature CS111 (0.000000)
31. feature 2SEMESTER (0.000000)
32. feature AT316 (0.000000)
33. feature AT326 (0.000000)
34. feature BA291 (0.000000)
35. feature CJ315 (0.000000)
36. feature CJ316 (0.000000)
37. feature CJ317 (0.000000)
38. feature CJ321 (0.000000)
39. feature CS101 (0.000000)
40. feature CS102 (0.000000)
41. feature CS105 (0.000000)
42. feature CS115 (0.000000)
43. feature CS285 (0.000000)
44. feature CS211 (0.000000)
45. feature CS213 (0.000000)
46. feature CS214 (0.000000)
47. feature CS215 (0.000000)
48. feature CS222 (0.000000)
49. feature CS223 (0.000000)
50. feature CS231 (0.000000)
51. feature CS251 (0.000000)
52. feature CS261 (0.000000)
53. feature CS281 (0.000000)
54. feature CS284 (0.000000)
55. feature CS397 (0.000000)
56. feature CS399 (0.000000)
57. feature TU120 (0.000000)
58. feature ST216 (0.000000)
59. feature MA216 (0.000000)
60. feature MA332 (0.000000)
61. feature MW313 (0.000000)
62. feature MW314 (0.000000)
63. feature NS132 (0.000000)
64. feature PY228 (0.000000)
65. feature SC123 (0.000000)
66. feature SC135 (0.000000)
67. feature SC173 (0.000000)
68. feature SC185 (0.000000)
69. feature SO201 (0.000000)
70. feature SW111 (0.000000)
71. feature MA211 (0.000000)
72. feature SW212 (0.000000)
73. feature SW213 (0.000000)
74. feature SW221 (0.000000)
75. feature SW335 (0.000000)
76. feature SW365 (0.000000)
77. feature SW475 (0.000000)
78. feature SW478 (0.000000)
79. feature TA395 (0.000000)
80. feature TH161 (0.000000)
81. feature TU100 (0.000000)
82. feature TU110 (0.000000)
83. feature MA212 (0.000000)
84. feature LA209 (0.000000)
85. feature CS401 (0.000000)
86. feature CS459 (0.000000)
87. feature CS402 (0.000000)
88. feature CS407 (0.000000)
89. feature CS408 (0.000000)
90. feature CS409 (0.000000)
91. feature CS426 (0.000000)
92. feature CS427 (0.000000)
93. feature CS429 (0.000000)
94. feature CS446 (0.000000)
95. feature CS449 (0.000000)
96. feature CS456 (0.000000)
97. feature CS457 (0.000000)
98. feature CS467 (0.000000)
99. feature HR201 (0.000000)
100. feature CS486 (0.000000)
101. feature CS487 (0.000000)
102. feature CS488 (0.000000)
103. feature CS489 (0.000000)
104. feature EL070 (0.000000)
105. feature EL171 (0.000000)
106. feature EL172 (0.000000)
107. feature EL295 (0.000000)
108. feature EL395 (0.000000)
109. feature ES356 (0.000000)
110. feature HO201 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'TU122', u'CS348', u'CS295', u'CS296', u'CS297', u'CS300', u'CS301',
u'CS302', u'CS311', u'CS314'],
dtype='object')
-----------------------------------
CS111
[ 0.44117647 0.4047619 0.42771084 0.41818182 0.38181818]
Random Forest Cross Validation of CS111: 0.414729843745
Feature ranking:
1. feature TU122 (0.120268)
2. feature CS489 (0.105011)
3. feature CJ321 (0.104823)
4. feature HR201 (0.084572)
5. feature SW478 (0.083900)
6. feature PY228 (0.068182)
7. feature CJ317 (0.064315)
8. feature CS101 (0.048129)
9. feature SC135 (0.037484)
10. feature CS488 (0.029377)
11. feature CS261 (0.028934)
12. feature EL070 (0.023078)
13. feature TA395 (0.015779)
14. feature TH161 (0.014448)
15. feature SW111 (0.013083)
16. feature NS132 (0.009707)
17. feature LA209 (0.009687)
18. feature CS102 (0.009594)
19. feature TU120 (0.008619)
20. feature EL395 (0.008618)
21. feature TU100 (0.008437)
22. feature EL171 (0.008384)
23. feature SC123 (0.008100)
24. feature CS115 (0.006607)
25. feature CS214 (0.005715)
26. feature CS211 (0.005232)
27. feature CS223 (0.005048)
28. feature SC185 (0.004848)
29. feature MW314 (0.004499)
30. feature AT326 (0.003837)
31. feature CS251 (0.003638)
32. feature SW335 (0.003235)
33. feature MA332 (0.003172)
34. feature MA211 (0.002982)
35. feature ST216 (0.002960)
36. feature CS215 (0.002760)
37. feature MA212 (0.002320)
38. feature EL172 (0.002095)
39. feature CS398 (0.001946)
40. feature CJ315 (0.001848)
41. feature CS302 (0.001794)
42. feature SW213 (0.001694)
43. feature SC173 (0.001641)
44. feature MA216 (0.001508)
45. feature TU110 (0.001403)
46. feature 1ACADYEAR (0.001358)
47. feature SW212 (0.001280)
48. feature CS231 (0.001234)
49. feature CS297 (0.001190)
50. feature SO201 (0.001114)
51. feature BA291 (0.001033)
52. feature 2SEMESTER (0.001001)
53. feature CS356 (0.000931)
54. feature SW221 (0.000811)
55. feature CS486 (0.000579)
56. feature EL295 (0.000549)
57. feature CS366 (0.000534)
58. feature CS300 (0.000522)
59. feature CS284 (0.000492)
60. feature CS387 (0.000478)
61. feature CS285 (0.000438)
62. feature CS295 (0.000428)
63. feature CS467 (0.000425)
64. feature CS348 (0.000400)
65. feature CS365 (0.000396)
66. feature CS326 (0.000321)
67. feature CS446 (0.000299)
68. feature CS301 (0.000275)
69. feature CS374 (0.000272)
70. feature CS459 (0.000167)
71. feature CS286 (0.000165)
72. feature CS367 (0.000000)
73. feature CS105 (0.000000)
74. feature CS487 (0.000000)
75. feature CS402 (0.000000)
76. feature CS213 (0.000000)
77. feature ES356 (0.000000)
78. feature HO201 (0.000000)
79. feature CS111 (0.000000)
80. feature MW313 (0.000000)
81. feature CS401 (0.000000)
82. feature CS407 (0.000000)
83. feature CS408 (0.000000)
84. feature CS409 (0.000000)
85. feature CJ316 (0.000000)
86. feature SW365 (0.000000)
87. feature CS426 (0.000000)
88. feature SW475 (0.000000)
89. feature AT316 (0.000000)
90. feature CS427 (0.000000)
91. feature CS222 (0.000000)
92. feature CS456 (0.000000)
93. feature CS449 (0.000000)
94. feature CS457 (0.000000)
95. feature CS377 (0.000000)
96. feature CS385 (0.000000)
97. feature CS342 (0.000000)
98. feature CS341 (0.000000)
99. feature CS386 (0.000000)
100. feature CS314 (0.000000)
101. feature CS311 (0.000000)
102. feature CS429 (0.000000)
103. feature CS281 (0.000000)
104. feature CS388 (0.000000)
105. feature CS296 (0.000000)
106. feature CS395 (0.000000)
107. feature CS289 (0.000000)
108. feature CS288 (0.000000)
109. feature CS396 (0.000000)
110. feature CS397 (0.000000)
111. feature CS399 (0.000000)
Index([u'TU122', u'CS489', u'CJ321', u'HR201', u'SW478', u'PY228', u'CJ317',
u'CS101', u'SC135', u'CS488'],
dtype='object')
-----------------------------------
CS115
[ 0.4 0.625 0.5 1. 0.8 ]
Random Forest Cross Validation of CS115: 0.665
Feature ranking:
1. feature CJ321 (0.258183)
2. feature CS101 (0.134758)
3. feature CJ317 (0.133041)
4. feature CS489 (0.097015)
5. feature SC135 (0.091690)
6. feature TA395 (0.086223)
7. feature SW478 (0.050148)
8. feature MA211 (0.037288)
9. feature TU122 (0.033856)
10. feature HR201 (0.025374)
11. feature PY228 (0.022564)
12. feature CS488 (0.012743)
13. feature EL070 (0.011259)
14. feature SC123 (0.005857)
15. feature CS356 (0.000000)
16. feature CS300 (0.000000)
17. feature CS385 (0.000000)
18. feature CS377 (0.000000)
19. feature CS289 (0.000000)
20. feature CS295 (0.000000)
21. feature CS374 (0.000000)
22. feature CS296 (0.000000)
23. feature CS367 (0.000000)
24. feature CS297 (0.000000)
25. feature CS301 (0.000000)
26. feature CS348 (0.000000)
27. feature CS302 (0.000000)
28. feature CS311 (0.000000)
29. feature CS314 (0.000000)
30. feature CS366 (0.000000)
31. feature CS365 (0.000000)
32. feature CS288 (0.000000)
33. feature CS341 (0.000000)
34. feature CS342 (0.000000)
35. feature CS326 (0.000000)
36. feature CS261 (0.000000)
37. feature CS286 (0.000000)
38. feature CS285 (0.000000)
39. feature 2SEMESTER (0.000000)
40. feature AT316 (0.000000)
41. feature AT326 (0.000000)
42. feature BA291 (0.000000)
43. feature CJ315 (0.000000)
44. feature CJ316 (0.000000)
45. feature CS102 (0.000000)
46. feature CS105 (0.000000)
47. feature CS111 (0.000000)
48. feature CS115 (0.000000)
49. feature CS211 (0.000000)
50. feature CS213 (0.000000)
51. feature CS214 (0.000000)
52. feature CS215 (0.000000)
53. feature CS222 (0.000000)
54. feature CS223 (0.000000)
55. feature CS231 (0.000000)
56. feature CS251 (0.000000)
57. feature CS387 (0.000000)
58. feature CS281 (0.000000)
59. feature CS284 (0.000000)
60. feature CS386 (0.000000)
61. feature CS399 (0.000000)
62. feature CS388 (0.000000)
63. feature SO201 (0.000000)
64. feature HO201 (0.000000)
65. feature LA209 (0.000000)
66. feature MA212 (0.000000)
67. feature MA216 (0.000000)
68. feature MA332 (0.000000)
69. feature MW313 (0.000000)
70. feature MW314 (0.000000)
71. feature NS132 (0.000000)
72. feature SC173 (0.000000)
73. feature SC185 (0.000000)
74. feature ST216 (0.000000)
75. feature CS395 (0.000000)
76. feature SW111 (0.000000)
77. feature SW212 (0.000000)
78. feature SW213 (0.000000)
79. feature SW221 (0.000000)
80. feature SW335 (0.000000)
81. feature SW365 (0.000000)
82. feature SW475 (0.000000)
83. feature TH161 (0.000000)
84. feature TU100 (0.000000)
85. feature TU110 (0.000000)
86. feature ES356 (0.000000)
87. feature EL395 (0.000000)
88. feature EL295 (0.000000)
89. feature EL172 (0.000000)
90. feature CS396 (0.000000)
91. feature CS397 (0.000000)
92. feature CS398 (0.000000)
93. feature TU120 (0.000000)
94. feature CS401 (0.000000)
95. feature CS402 (0.000000)
96. feature CS407 (0.000000)
97. feature CS408 (0.000000)
98. feature CS409 (0.000000)
99. feature CS426 (0.000000)
100. feature CS427 (0.000000)
101. feature CS429 (0.000000)
102. feature CS446 (0.000000)
103. feature CS449 (0.000000)
104. feature CS456 (0.000000)
105. feature CS457 (0.000000)
106. feature CS459 (0.000000)
107. feature CS467 (0.000000)
108. feature CS486 (0.000000)
109. feature CS487 (0.000000)
110. feature EL171 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'CJ321', u'CS101', u'CJ317', u'CS489', u'SC135', u'TA395', u'SW478',
u'MA211', u'TU122', u'HR201'],
dtype='object')
-----------------------------------
CS211
[ 0.4137931 0.40740741 0.55555556 0.4 0.54166667]
Random Forest Cross Validation of CS211: 0.463684546616
Feature ranking:
1. feature CS102 (0.179454)
2. feature CS105 (0.073212)
3. feature TU122 (0.070786)
4. feature CS489 (0.056159)
5. feature PY228 (0.052717)
6. feature HR201 (0.051527)
7. feature EL070 (0.049865)
8. feature MW314 (0.049130)
9. feature TH161 (0.046116)
10. feature NS132 (0.044781)
11. feature SW478 (0.043106)
12. feature CS101 (0.035429)
13. feature CS488 (0.028440)
14. feature SC185 (0.027808)
15. feature CJ321 (0.023654)
16. feature TU100 (0.023155)
17. feature SO201 (0.020360)
18. feature TU120 (0.019560)
19. feature TA395 (0.019464)
20. feature CJ317 (0.018381)
21. feature MA211 (0.015108)
22. feature SC135 (0.014962)
23. feature LA209 (0.008177)
24. feature MW313 (0.008066)
25. feature SW475 (0.006294)
26. feature SW212 (0.005632)
27. feature SC123 (0.003536)
28. feature TU110 (0.002139)
29. feature EL295 (0.001976)
30. feature CS215 (0.001005)
31. feature CJ316 (0.000000)
32. feature CS301 (0.000000)
33. feature CS289 (0.000000)
34. feature CS295 (0.000000)
35. feature CS296 (0.000000)
36. feature CS297 (0.000000)
37. feature 2SEMESTER (0.000000)
38. feature CS300 (0.000000)
39. feature CS302 (0.000000)
40. feature CS286 (0.000000)
41. feature CS311 (0.000000)
42. feature CS314 (0.000000)
43. feature CS326 (0.000000)
44. feature CS341 (0.000000)
45. feature CS342 (0.000000)
46. feature CS348 (0.000000)
47. feature CS356 (0.000000)
48. feature CS288 (0.000000)
49. feature CS285 (0.000000)
50. feature CJ315 (0.000000)
51. feature CS284 (0.000000)
52. feature CS111 (0.000000)
53. feature CS115 (0.000000)
54. feature CS366 (0.000000)
55. feature CS211 (0.000000)
56. feature CS213 (0.000000)
57. feature BA291 (0.000000)
58. feature AT326 (0.000000)
59. feature AT316 (0.000000)
60. feature CS214 (0.000000)
61. feature CS222 (0.000000)
62. feature CS223 (0.000000)
63. feature CS231 (0.000000)
64. feature CS251 (0.000000)
65. feature CS261 (0.000000)
66. feature CS281 (0.000000)
67. feature CS365 (0.000000)
68. feature CS399 (0.000000)
69. feature CS367 (0.000000)
70. feature MA212 (0.000000)
71. feature CS467 (0.000000)
72. feature CS486 (0.000000)
73. feature CS487 (0.000000)
74. feature EL171 (0.000000)
75. feature EL172 (0.000000)
76. feature EL395 (0.000000)
77. feature ES356 (0.000000)
78. feature HO201 (0.000000)
79. feature MA216 (0.000000)
80. feature CS457 (0.000000)
81. feature MA332 (0.000000)
82. feature SC173 (0.000000)
83. feature ST216 (0.000000)
84. feature SW111 (0.000000)
85. feature SW213 (0.000000)
86. feature SW221 (0.000000)
87. feature SW335 (0.000000)
88. feature SW365 (0.000000)
89. feature CS459 (0.000000)
90. feature CS456 (0.000000)
91. feature CS374 (0.000000)
92. feature CS398 (0.000000)
93. feature CS377 (0.000000)
94. feature CS385 (0.000000)
95. feature CS386 (0.000000)
96. feature CS387 (0.000000)
97. feature CS388 (0.000000)
98. feature CS395 (0.000000)
99. feature CS396 (0.000000)
100. feature CS397 (0.000000)
101. feature CS401 (0.000000)
102. feature CS449 (0.000000)
103. feature CS402 (0.000000)
104. feature CS407 (0.000000)
105. feature CS408 (0.000000)
106. feature CS409 (0.000000)
107. feature CS426 (0.000000)
108. feature CS427 (0.000000)
109. feature CS429 (0.000000)
110. feature CS446 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'CS102', u'CS105', u'TU122', u'CS489', u'PY228', u'HR201', u'EL070',
u'MW314', u'TH161', u'NS132'],
dtype='object')
-----------------------------------
CS213
[ 0.47741935 0.47402597 0.48684211 0.48026316 0.44078947]
Random Forest Cross Validation of CS213: 0.471868013141
Feature ranking:
1. feature CS102 (0.124877)
2. feature CS101 (0.063764)
3. feature CJ321 (0.059738)
4. feature HR201 (0.050456)
5. feature EL070 (0.049631)
6. feature TU122 (0.045934)
7. feature CS489 (0.043056)
8. feature SW478 (0.040042)
9. feature TH161 (0.038295)
10. feature MW314 (0.037872)
11. feature PY228 (0.037832)
12. feature CJ317 (0.032103)
13. feature TU100 (0.031269)
14. feature SC185 (0.026714)
15. feature SC135 (0.024752)
16. feature TU120 (0.018082)
17. feature CS488 (0.017180)
18. feature LA209 (0.014903)
19. feature TA395 (0.012055)
20. feature SW213 (0.011927)
21. feature ST216 (0.010054)
22. feature TU110 (0.009750)
23. feature CS348 (0.009585)
24. feature CS302 (0.009132)
25. feature HO201 (0.008901)
26. feature SC173 (0.008305)
27. feature EL395 (0.007687)
28. feature SO201 (0.007618)
29. feature 1ACADYEAR (0.007354)
30. feature CS115 (0.007220)
31. feature CS214 (0.006509)
32. feature MA332 (0.006225)
33. feature CS314 (0.005771)
34. feature CS251 (0.005680)
35. feature MA216 (0.005656)
36. feature CS211 (0.005456)
37. feature SC123 (0.005451)
38. feature BA291 (0.005408)
39. feature EL171 (0.005323)
40. feature CS297 (0.005259)
41. feature CS223 (0.004982)
42. feature CS215 (0.004907)
43. feature NS132 (0.004888)
44. feature MA212 (0.004699)
45. feature SW111 (0.004304)
46. feature CS261 (0.004250)
47. feature CS387 (0.003254)
48. feature CS105 (0.002975)
49. feature 2SEMESTER (0.002969)
50. feature SW212 (0.002963)
51. feature CS487 (0.002917)
52. feature CS231 (0.002489)
53. feature EL172 (0.002455)
54. feature SW335 (0.002438)
55. feature EL295 (0.002398)
56. feature CS402 (0.002323)
57. feature CS285 (0.002191)
58. feature CJ315 (0.002000)
59. feature AT326 (0.001984)
60. feature MA211 (0.001893)
61. feature SW475 (0.001684)
62. feature SW221 (0.001571)
63. feature CS374 (0.001390)
64. feature CS366 (0.001218)
65. feature CS459 (0.001117)
66. feature CS326 (0.000994)
67. feature CS286 (0.000878)
68. feature CS365 (0.000844)
69. feature CS377 (0.000629)
70. feature CS467 (0.000619)
71. feature CS300 (0.000558)
72. feature CS301 (0.000451)
73. feature CS311 (0.000406)
74. feature CS486 (0.000368)
75. feature CS356 (0.000348)
76. feature MW313 (0.000309)
77. feature CS284 (0.000269)
78. feature SW365 (0.000159)
79. feature CS289 (0.000087)
80. feature CS449 (0.000000)
81. feature CS213 (0.000000)
82. feature CS446 (0.000000)
83. feature CS456 (0.000000)
84. feature CS388 (0.000000)
85. feature CS111 (0.000000)
86. feature CS457 (0.000000)
87. feature CS222 (0.000000)
88. feature CJ316 (0.000000)
89. feature AT316 (0.000000)
90. feature ES356 (0.000000)
91. feature CS429 (0.000000)
92. feature CS407 (0.000000)
93. feature CS427 (0.000000)
94. feature CS398 (0.000000)
95. feature CS386 (0.000000)
96. feature CS385 (0.000000)
97. feature CS367 (0.000000)
98. feature CS396 (0.000000)
99. feature CS342 (0.000000)
100. feature CS341 (0.000000)
101. feature CS397 (0.000000)
102. feature CS401 (0.000000)
103. feature CS426 (0.000000)
104. feature CS296 (0.000000)
105. feature CS295 (0.000000)
106. feature CS288 (0.000000)
107. feature CS395 (0.000000)
108. feature CS281 (0.000000)
109. feature CS408 (0.000000)
110. feature CS409 (0.000000)
111. feature CS399 (0.000000)
Index([u'CS102', u'CS101', u'CJ321', u'HR201', u'EL070', u'TU122', u'CS489',
u'SW478', u'TH161', u'MW314'],
dtype='object')
-----------------------------------
CS214
[ 0.48 0.44 0.44444444 0.45263158 0.46808511]
Random Forest Cross Validation of CS214: 0.457032225955
Feature ranking:
1. feature CS102 (0.074440)
2. feature TU100 (0.064593)
3. feature PY228 (0.059186)
4. feature HR201 (0.058445)
5. feature TU122 (0.057157)
6. feature CS489 (0.054551)
7. feature TH161 (0.052811)
8. feature CJ321 (0.051975)
9. feature MW314 (0.051932)
10. feature EL070 (0.048937)
11. feature SW478 (0.046040)
12. feature SC135 (0.044265)
13. feature CJ317 (0.037967)
14. feature HO201 (0.026122)
15. feature LA209 (0.021944)
16. feature TU110 (0.020597)
17. feature SW111 (0.016580)
18. feature SC173 (0.016351)
19. feature SW213 (0.015756)
20. feature TU120 (0.014864)
21. feature SO201 (0.014848)
22. feature CS488 (0.014787)
23. feature ST216 (0.012846)
24. feature MA332 (0.012122)
25. feature SC123 (0.009658)
26. feature NS132 (0.009232)
27. feature SC185 (0.008492)
28. feature SW335 (0.008200)
29. feature TA395 (0.007941)
30. feature MA216 (0.006582)
31. feature 1ACADYEAR (0.005560)
32. feature EL295 (0.005218)
33. feature EL395 (0.004289)
34. feature CS261 (0.003580)
35. feature CS231 (0.003487)
36. feature 2SEMESTER (0.003398)
37. feature SW212 (0.003158)
38. feature CJ315 (0.002982)
39. feature CS214 (0.002864)
40. feature EL172 (0.002725)
41. feature SW475 (0.002611)
42. feature EL171 (0.002299)
43. feature CS314 (0.002285)
44. feature CS115 (0.002266)
45. feature SW365 (0.001767)
46. feature CS211 (0.001750)
47. feature CS223 (0.001635)
48. feature MA212 (0.001488)
49. feature BA291 (0.001288)
50. feature SW221 (0.000996)
51. feature CS215 (0.000910)
52. feature CS311 (0.000902)
53. feature CS297 (0.000824)
54. feature CS289 (0.000786)
55. feature CS341 (0.000632)
56. feature CS302 (0.000561)
57. feature CS356 (0.000514)
58. feature CS213 (0.000000)
59. feature AT316 (0.000000)
60. feature CS301 (0.000000)
61. feature AT326 (0.000000)
62. feature CS300 (0.000000)
63. feature CS296 (0.000000)
64. feature CS295 (0.000000)
65. feature CS288 (0.000000)
66. feature CS286 (0.000000)
67. feature CS285 (0.000000)
68. feature CS284 (0.000000)
69. feature CS281 (0.000000)
70. feature CJ316 (0.000000)
71. feature CS251 (0.000000)
72. feature CS101 (0.000000)
73. feature CS105 (0.000000)
74. feature CS111 (0.000000)
75. feature CS222 (0.000000)
76. feature ES356 (0.000000)
77. feature CS342 (0.000000)
78. feature CS326 (0.000000)
79. feature CS456 (0.000000)
80. feature CS409 (0.000000)
81. feature CS426 (0.000000)
82. feature CS427 (0.000000)
83. feature CS429 (0.000000)
84. feature CS446 (0.000000)
85. feature CS449 (0.000000)
86. feature CS457 (0.000000)
87. feature CS348 (0.000000)
88. feature CS459 (0.000000)
89. feature CS467 (0.000000)
90. feature CS486 (0.000000)
91. feature CS487 (0.000000)
92. feature MW313 (0.000000)
93. feature MA211 (0.000000)
94. feature CS408 (0.000000)
95. feature CS407 (0.000000)
96. feature CS402 (0.000000)
97. feature CS401 (0.000000)
98. feature CS398 (0.000000)
99. feature CS397 (0.000000)
100. feature CS396 (0.000000)
101. feature CS395 (0.000000)
102. feature CS388 (0.000000)
103. feature CS387 (0.000000)
104. feature CS386 (0.000000)
105. feature CS385 (0.000000)
106. feature CS377 (0.000000)
107. feature CS374 (0.000000)
108. feature CS367 (0.000000)
109. feature CS366 (0.000000)
110. feature CS365 (0.000000)
111. feature CS399 (0.000000)
Index([u'CS102', u'TU100', u'PY228', u'HR201', u'TU122', u'CS489', u'TH161',
u'CJ321', u'MW314', u'EL070'],
dtype='object')
-----------------------------------
CS215
[ 0.44444444 0.5 0.5 0.64285714 0.57142857]
Random Forest Cross Validation of CS215: 0.531746031746
Feature ranking:
1. feature CJ321 (0.134913)
2. feature CS489 (0.080799)
3. feature TU120 (0.080726)
4. feature CS102 (0.079654)
5. feature TH161 (0.063425)
6. feature TU122 (0.060609)
7. feature PY228 (0.060175)
8. feature HR201 (0.045563)
9. feature CS105 (0.044111)
10. feature SW478 (0.040173)
11. feature SC135 (0.039302)
12. feature MW314 (0.037317)
13. feature TA395 (0.036471)
14. feature EL070 (0.031730)
15. feature TU100 (0.024245)
16. feature MA211 (0.023920)
17. feature SC185 (0.023480)
18. feature CS101 (0.023387)
19. feature CJ317 (0.014852)
20. feature SO201 (0.011111)
21. feature CS488 (0.009870)
22. feature NS132 (0.007936)
23. feature SC123 (0.006243)
24. feature SW212 (0.004715)
25. feature EL295 (0.004672)
26. feature SW475 (0.004595)
27. feature SC173 (0.003387)
28. feature LA209 (0.002618)
29. feature CS301 (0.000000)
30. feature CS288 (0.000000)
31. feature CS289 (0.000000)
32. feature CS295 (0.000000)
33. feature CS296 (0.000000)
34. feature CS297 (0.000000)
35. feature CS300 (0.000000)
36. feature 2SEMESTER (0.000000)
37. feature CS302 (0.000000)
38. feature CS285 (0.000000)
39. feature CS311 (0.000000)
40. feature CS314 (0.000000)
41. feature CS326 (0.000000)
42. feature CS341 (0.000000)
43. feature CS342 (0.000000)
44. feature CS348 (0.000000)
45. feature CS356 (0.000000)
46. feature CS286 (0.000000)
47. feature CS281 (0.000000)
48. feature CS284 (0.000000)
49. feature CS214 (0.000000)
50. feature CJ315 (0.000000)
51. feature BA291 (0.000000)
52. feature CS111 (0.000000)
53. feature CS115 (0.000000)
54. feature CS211 (0.000000)
55. feature CS366 (0.000000)
56. feature CS213 (0.000000)
57. feature AT326 (0.000000)
58. feature CJ316 (0.000000)
59. feature AT316 (0.000000)
60. feature CS215 (0.000000)
61. feature CS222 (0.000000)
62. feature CS223 (0.000000)
63. feature CS231 (0.000000)
64. feature CS251 (0.000000)
65. feature CS261 (0.000000)
66. feature CS365 (0.000000)
67. feature CS399 (0.000000)
68. feature CS367 (0.000000)
69. feature CS374 (0.000000)
70. feature CS467 (0.000000)
71. feature CS486 (0.000000)
72. feature CS487 (0.000000)
73. feature EL171 (0.000000)
74. feature EL172 (0.000000)
75. feature EL395 (0.000000)
76. feature ES356 (0.000000)
77. feature HO201 (0.000000)
78. feature MA212 (0.000000)
79. feature MA216 (0.000000)
80. feature MA332 (0.000000)
81. feature MW313 (0.000000)
82. feature ST216 (0.000000)
83. feature SW111 (0.000000)
84. feature SW213 (0.000000)
85. feature SW221 (0.000000)
86. feature SW335 (0.000000)
87. feature SW365 (0.000000)
88. feature TU110 (0.000000)
89. feature CS459 (0.000000)
90. feature CS457 (0.000000)
91. feature CS456 (0.000000)
92. feature CS398 (0.000000)
93. feature CS377 (0.000000)
94. feature CS385 (0.000000)
95. feature CS386 (0.000000)
96. feature CS387 (0.000000)
97. feature CS388 (0.000000)
98. feature CS395 (0.000000)
99. feature CS396 (0.000000)
100. feature CS397 (0.000000)
101. feature CS401 (0.000000)
102. feature CS449 (0.000000)
103. feature CS402 (0.000000)
104. feature CS407 (0.000000)
105. feature CS408 (0.000000)
106. feature CS409 (0.000000)
107. feature CS426 (0.000000)
108. feature CS427 (0.000000)
109. feature CS429 (0.000000)
110. feature CS446 (0.000000)
111. feature 1ACADYEAR (0.000000)
Index([u'CJ321', u'CS489', u'TU120', u'CS102', u'TH161', u'TU122', u'PY228',
u'HR201', u'CS105', u'SW478'],
dtype='object')
-----------------------------------
CS222
[ 0.51 0.52 0.56122449 0.5257732 0.47368421]
Random Forest Cross Validation of CS222: 0.51813637924
Feature ranking:
1. feature CS102 (0.102808)
2. feature TH161 (0.074865)
3. feature CJ317 (0.068900)
4. feature PY228 (0.063224)
5. feature TU122 (0.062739)
6. feature HR201 (0.062516)
7. feature CS489 (0.048046)
8. feature CJ321 (0.046887)
9. feature EL070 (0.043379)
10. feature TU100 (0.042148)
11. feature MW314 (0.040812)
12. feature SW478 (0.039911)
13. feature SC135 (0.029227)
14. feature CS115 (0.018117)
15. feature HO201 (0.018011)
16. feature CS488 (0.015096)
17. feature SW213 (0.014847)
18. feature MA216 (0.014597)
19. feature SW111 (0.014492)
20. feature SO201 (0.013572)
21. feature SC185 (0.012526)
22. feature LA209 (0.011194)
23. feature SC173 (0.011007)
24. feature TU120 (0.010746)
25. feature ST216 (0.009969)
26. feature MA332 (0.009511)
27. feature 1ACADYEAR (0.008692)
28. feature TU110 (0.008168)
29. feature EL295 (0.007671)
30. feature EL171 (0.006873)
31. feature CJ315 (0.006813)
32. feature SC123 (0.006792)
33. feature TA395 (0.006754)
34. feature CS211 (0.005452)
35. feature NS132 (0.005439)
36. feature SW335 (0.005308)
37. feature SW212 (0.005106)
38. feature CS261 (0.004788)
39. feature SW221 (0.004727)
40. feature MW313 (0.002851)
41. feature AT326 (0.002026)
42. feature BA291 (0.001838)
43. feature CS251 (0.001742)
44. feature CS231 (0.001716)
45. feature CS223 (0.001477)
46. feature CS214 (0.001330)
47. feature MA212 (0.001302)
48. feature SW475 (0.001278)
49. feature EL395 (0.001182)
50. feature CS301 (0.000547)
51. feature CS215 (0.000394)
52. feature CS326 (0.000297)
53. feature CS487 (0.000290)
54. feature CS295 (0.000000)
55. feature CS289 (0.000000)
56. feature CS288 (0.000000)
57. feature CS296 (0.000000)
58. feature CS297 (0.000000)
59. feature CS286 (0.000000)
60. feature CS284 (0.000000)
61. feature CS300 (0.000000)
62. feature CS285 (0.000000)
63. feature MA211 (0.000000)
64. feature CS281 (0.000000)
65. feature CS222 (0.000000)
66. feature CS213 (0.000000)
67. feature CS311 (0.000000)
68. feature SW365 (0.000000)
69. feature CS111 (0.000000)
70. feature CS105 (0.000000)
71. feature CS101 (0.000000)
72. feature CJ316 (0.000000)
73. feature AT316 (0.000000)
74. feature 2SEMESTER (0.000000)
75. feature CS302 (0.000000)
76. feature CS356 (0.000000)
77. feature CS314 (0.000000)
78. feature CS449 (0.000000)
79. feature CS408 (0.000000)
80. feature CS409 (0.000000)
81. feature CS426 (0.000000)
82. feature CS427 (0.000000)
83. feature CS429 (0.000000)
84. feature CS446 (0.000000)
85. feature CS456 (0.000000)
86. feature CS402 (0.000000)
87. feature CS457 (0.000000)
88. feature CS459 (0.000000)
89. feature CS467 (0.000000)
90. feature CS486 (0.000000)
91. feature EL172 (0.000000)
92. feature ES356 (0.000000)
93. feature CS407 (0.000000)
94. feature CS401 (0.000000)
95. feature CS341 (0.000000)
96. feature CS377 (0.000000)
97. feature CS342 (0.000000)
98. feature CS348 (0.000000)
99. feature CS365 (0.000000)
100. feature CS366 (0.000000)
101. feature CS367 (0.000000)
102. feature CS374 (0.000000)
103. feature CS385 (0.000000)
104. feature CS398 (0.000000)
105. feature CS386 (0.000000)
106. feature CS387 (0.000000)
107. feature CS388 (0.000000)
108. feature CS395 (0.000000)
109. feature CS396 (0.000000)
110. feature CS397 (0.000000)
111. feature CS399 (0.000000)
Index([u'CS102', u'TH161', u'CJ317', u'PY228', u'TU122', u'HR201', u'CS489',
u'CJ321', u'EL070', u'TU100'],
dtype='object')
-----------------------------------
CS223
[ 0.55147059 0.53731343 0.57894737 0.58778626 0.53846154]
Random Forest Cross Validation of CS223: 0.558795837499
Feature ranking:
1. feature CS102 (0.104677)
2. feature TH161 (0.059489)
3. feature EL070 (0.055731)
4. feature PY228 (0.053841)
5. feature HR201 (0.053772)
6. feature TU122 (0.052969)
7. feature CJ317 (0.045959)
8. feature SW478 (0.043770)
9. feature MW314 (0.041636)
10. feature CS489 (0.041549)
11. feature TU100 (0.040536)
12. feature CJ321 (0.033200)
13. feature SC135 (0.031886)
14. feature LA209 (0.026277)
15. feature CS488 (0.021741)
16. feature SO201 (0.021067)
17. feature HO201 (0.020004)
18. feature CS101 (0.018410)
19. feature TU120 (0.017780)
20. feature NS132 (0.016868)
21. feature ST216 (0.016024)
22. feature TU110 (0.015239)
23. feature TA395 (0.013641)
24. feature SW213 (0.012288)
25. feature SC185 (0.011949)
26. feature SC173 (0.010642)
27. feature CS302 (0.009770)
28. feature MA216 (0.008228)
29. feature EL295 (0.008185)
30. feature SC123 (0.007524)
31. feature 1ACADYEAR (0.007373)
32. feature EL395 (0.005417)
33. feature BA291 (0.005326)
34. feature SW111 (0.004386)
35. feature SW365 (0.003955)
36. feature MA212 (0.003852)
37. feature CS366 (0.003728)
38. feature CS301 (0.003670)
39. feature MA211 (0.003602)
40. feature CS261 (0.003193)
41. feature MA332 (0.002997)
42. feature SW212 (0.002795)
43. feature EL172 (0.002793)
44. feature CS115 (0.002780)
45. feature CS214 (0.002658)
46. feature EL171 (0.002349)
47. feature CS105 (0.001931)
48. feature CS285 (0.001736)
49. feature CS356 (0.001540)
50. feature CS467 (0.001510)
51. feature CS231 (0.001433)
52. feature CJ315 (0.001426)
53. feature SW335 (0.001370)
54. feature CS211 (0.001316)
55. feature MW313 (0.001294)
56. feature CS326 (0.001195)
57. feature CS215 (0.001125)
58. feature 2SEMESTER (0.001064)
59. feature SW475 (0.001063)
60. feature CS223 (0.001050)
61. feature CS396 (0.000831)
62. feature SW221 (0.000746)
63. feature CS314 (0.000614)
64. feature CS456 (0.000493)
65. feature CS297 (0.000471)
66. feature CS286 (0.000452)
67. feature CS284 (0.000445)
68. feature CS367 (0.000388)
69. feature CS311 (0.000366)
70. feature CS251 (0.000364)
71. feature CS295 (0.000256)
72. feature CS288 (0.000000)
73. feature ES356 (0.000000)
74. feature CS213 (0.000000)
75. feature CS281 (0.000000)
76. feature CS222 (0.000000)
77. feature CS111 (0.000000)
78. feature CJ316 (0.000000)
79. feature AT326 (0.000000)
80. feature AT316 (0.000000)
81. feature CS289 (0.000000)
82. feature CS342 (0.000000)
83. feature CS296 (0.000000)
84. feature CS402 (0.000000)
85. feature CS487 (0.000000)
86. feature CS486 (0.000000)
87. feature CS459 (0.000000)
88. feature CS457 (0.000000)
89. feature CS449 (0.000000)
90. feature CS446 (0.000000)
91. feature CS429 (0.000000)
92. feature CS427 (0.000000)
93. feature CS426 (0.000000)
94. feature CS409 (0.000000)
95. feature CS408 (0.000000)
96. feature CS407 (0.000000)
97. feature CS401 (0.000000)
98. feature CS300 (0.000000)
99. feature CS398 (0.000000)
100. feature CS397 (0.000000)
101. feature CS395 (0.000000)
102. feature CS388 (0.000000)
103. feature CS387 (0.000000)
104. feature CS386 (0.000000)
105. feature CS385 (0.000000)
106. feature CS377 (0.000000)
107. feature CS374 (0.000000)
108. feature CS365 (0.000000)
109. feature CS348 (0.000000)
110. feature CS341 (0.000000)
111. feature CS399 (0.000000)
Index([u'CS102', u'TH161', u'EL070', u'PY228', u'HR201', u'TU122', u'CJ317',
u'SW478', u'MW314', u'CS489'],
dtype='object')
-----------------------------------
CS231
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-8-672ec9c80e12> in <module>()
68 min_samples_split=1, random_state=None, max_features=None)
69 clf = forest.fit(X, y)
---> 70 scores = cross_val_score(clf, X, y, cv=5)
71 print scores
72 print "Random Forest Cross Validation of %s: %s"%(subject,scores.mean())
C:\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
1350 X, y = indexable(X, y)
1351
-> 1352 cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
1353 scorer = check_scoring(estimator, scoring=scoring)
1354 # We clone the estimator to make sure that all the folds are
C:\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in _check_cv(cv, X, y, classifier, warn_mask)
1604 if classifier:
1605 if type_of_target(y) in ['binary', 'multiclass']:
-> 1606 cv = StratifiedKFold(y, cv, indices=needs_indices)
1607 else:
1608 cv = KFold(_num_samples(y), cv, indices=needs_indices)
C:\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in __init__(self, y, n_folds, indices, shuffle, random_state)
404 random_state=None):
405 super(StratifiedKFold, self).__init__(
--> 406 len(y), n_folds, indices, shuffle, random_state)
407 y = np.asarray(y)
408 n_samples = y.shape[0]
C:\Anaconda\lib\site-packages\sklearn\cross_validation.pyc in __init__(self, n, n_folds, indices, shuffle, random_state)
255 raise ValueError(
256 ("Cannot have number of folds n_folds={0} greater"
--> 257 " than the number of samples: {1}.").format(n_folds, n))
258
259 if not isinstance(shuffle, bool):
ValueError: Cannot have number of folds n_folds=5 greater than the number of samples: 1.
In [6]:
df_file = df_file.drop('Unnamed: 0',axis=1)
In [7]:
df_file
Out[7]:
Unnamed: 0.1
3COURSEID
4RESULT
0STUDENTID
1ACADYEAR
2SEMESTER
AT316
AT326
BA291
CJ315
...
TA395
TH161
TU100
TU110
TU120
TU122
TU130
TU154
PROVINCEID
SCHOOLGPA
0
0
CS101
6
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
1
1
CS102
6
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
2
2
EL171
5
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
3
3
SC135
4
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
4
4
SC185
6
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
5
5
TH161
6
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
6
6
TU154
5
316644
2552
1
0
0
0
0
...
0
0
0
0
0
0
0
0
12
3.32
7
7
CS111
5
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
8
8
EL172
4
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
9
9
MA211
4
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
10
10
PY228
7
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
11
11
TU110
6
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
12
12
TU120
5
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
13
13
TU130
7
316644
2552
2
0
0
0
0
...
0
6
0
0
0
0
0
5
12
3.32
14
14
TU122
7
316644
2552
3
0
0
0
0
...
0
6
0
6
5
0
7
5
12
3.32
15
15
AT326
8
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
16
16
CS213
6
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
17
17
CS214
7
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
18
18
CS222
7
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
19
19
CS223
7
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
20
20
CS284
7
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
21
21
MA211
5
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
22
22
SW111
5
316644
2553
1
0
0
0
0
...
0
6
0
6
5
7
7
5
12
3.32
23
23
AT316
7
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
24
24
CS251
6
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
25
25
CS261
7
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
26
26
CS281
7
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
27
27
MA332
6
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
28
28
SC135
6
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
29
29
ST216
6
316644
2553
2
0
8
0
0
...
0
6
0
6
5
7
7
5
12
3.32
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
27965
31292
EL070
2
447240
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
48
3.75
27966
31293
MA211
4
447240
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
48
3.75
27967
31294
ST216
4
447240
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
48
3.75
27968
31295
TH161
6
447240
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
48
3.75
27969
31296
TU154
4
447240
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
48
3.75
27970
31297
CS101
5
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27971
31298
CS102
5
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27972
31299
CS105
5
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27973
31300
EL070
2
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27974
31301
MA211
3
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27975
31302
ST216
3
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27976
31303
TH161
5
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27977
31304
TU154
3
447241
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
13
2.60
27978
31313
CS101
5
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27979
31314
CS102
5
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27980
31315
CS105
5
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27981
31316
EL171
3
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27982
31317
MA211
3
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27983
31318
ST216
3
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27984
31319
TH161
6
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27985
31320
TU154
5
447242
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
34
2.93
27986
31325
SC185
3
447242
2557
2
0
0
0
0
...
0
6
0
0
0
0
0
5
34
2.93
27987
31329
CS101
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27988
31330
CS102
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27989
31331
CS105
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27990
31332
EL070
1
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27991
31333
MA211
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27992
31334
ST216
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27993
31335
TH161
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27994
31336
TU154
4
447243
2557
1
0
0
0
0
...
0
0
0
0
0
0
0
0
84
2.08
27995 rows × 119 columns
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