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Stat_tests_ALL.txt
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Comparing CV results
knn VS svm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
svm median = 0.966798372841
----------------------------------------------------------
Comparing Train results
knn VS svm --> WilcoxonResult(statistic=0.0, pvalue=7.3861048569320701e-10)
knn median = 0.888888888889
svm median = 0.995
----------------------------------------------------------
Comparing Test results
knn VS svm --> WilcoxonResult(statistic=0.0, pvalue=7.5041245571439658e-10)
knn median = 0.730769230769
svm median = 0.98
----------------------------------------------------------
Comparing CV results
knn VS randomforests --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
randomforests median = 0.945144820219
----------------------------------------------------------
Comparing Train results
knn VS randomforests --> WilcoxonResult(statistic=0.0, pvalue=6.7879872141507655e-10)
knn median = 0.888888888889
randomforests median = 1.0
----------------------------------------------------------
Comparing Test results
knn VS randomforests --> WilcoxonResult(statistic=0.0, pvalue=7.5041245571439658e-10)
knn median = 0.730769230769
randomforests median = 0.94
----------------------------------------------------------
Comparing CV results
knn VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
NaiveBayes median = 0.25516999165
----------------------------------------------------------
Comparing Train results
knn VS NaiveBayes --> WilcoxonResult(statistic=4.0, pvalue=9.6086120281918218e-10)
knn median = 0.888888888889
NaiveBayes median = 0.94
----------------------------------------------------------
Comparing Test results
knn VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.5262280618946206e-10)
knn median = 0.730769230769
NaiveBayes median = 0.22
----------------------------------------------------------
Comparing CV results
knn VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
hmm median = 0.468582065488
----------------------------------------------------------
Comparing Train results
knn VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.888888888889
hmm median = 0.507890187807
----------------------------------------------------------
Comparing Test results
knn VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.730769230769
hmm median = 0.456295322665
----------------------------------------------------------
Comparing CV results
knn VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
gbrt median = 0.811905572756
----------------------------------------------------------
Comparing Train results
knn VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=6.7879872141507655e-10)
knn median = 0.888888888889
gbrt median = 1.0
----------------------------------------------------------
Comparing Test results
knn VS gbrt --> WilcoxonResult(statistic=112.0, pvalue=3.9133943644868418e-07)
knn median = 0.730769230769
gbrt median = 0.82
----------------------------------------------------------
Comparing CV results
knn VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
knn VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.4095745234010244e-10)
knn median = 0.888888888889
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
knn VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.4786929795741531e-10)
knn median = 0.730769230769
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
knn VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
knn median = 0.71182795699
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
knn VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5313371678781014e-10)
knn median = 0.888888888889
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
knn VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5415647264407807e-10)
knn median = 0.730769230769
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
svm VS randomforests --> WilcoxonResult(statistic=14.0, pvalue=1.7569391573831783e-09)
svm median = 0.966798372841
randomforests median = 0.945144820219
----------------------------------------------------------
Comparing Train results
svm VS randomforests --> WilcoxonResult(statistic=0.0, pvalue=9.0011723444509651e-10)
svm median = 0.995
randomforests median = 1.0
----------------------------------------------------------
Comparing Test results
svm VS randomforests --> WilcoxonResult(statistic=144.0, pvalue=0.00054226294764674174)
svm median = 0.98
randomforests median = 0.94
----------------------------------------------------------
Comparing CV results
svm VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.966798372841
NaiveBayes median = 0.25516999165
----------------------------------------------------------
Comparing Train results
svm VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.3160959914803643e-10)
svm median = 0.995
NaiveBayes median = 0.94
----------------------------------------------------------
Comparing Test results
svm VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.1177238692585937e-10)
svm median = 0.98
NaiveBayes median = 0.22
----------------------------------------------------------
Comparing CV results
svm VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.966798372841
hmm median = 0.468582065488
----------------------------------------------------------
Comparing Train results
svm VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.995
hmm median = 0.507890187807
----------------------------------------------------------
Comparing Test results
svm VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.98
hmm median = 0.456295322665
----------------------------------------------------------
Comparing CV results
svm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.966798372841
gbrt median = 0.811905572756
----------------------------------------------------------
Comparing Train results
svm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=9.0011723444509651e-10)
svm median = 0.995
gbrt median = 1.0
----------------------------------------------------------
Comparing Test results
svm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.1339331126943946e-10)
svm median = 0.98
gbrt median = 0.82
----------------------------------------------------------
Comparing CV results
svm VS logistic --> WilcoxonResult(statistic=78.0, pvalue=6.6266946103600768e-08)
svm median = 0.966798372841
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
svm VS logistic --> WilcoxonResult(statistic=18.0, pvalue=3.4171083200671256e-08)
svm median = 0.995
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
svm VS logistic --> WilcoxonResult(statistic=254.0, pvalue=0.02011315732662047)
svm median = 0.98
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
svm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
svm median = 0.966798372841
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
svm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.3610329094844772e-10)
svm median = 0.995
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
svm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.1843927522842306e-10)
svm median = 0.98
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
randomforests VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 0.945144820219
NaiveBayes median = 0.25516999165
----------------------------------------------------------
Comparing Train results
randomforests VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.2351728231245897e-10)
randomforests median = 1.0
NaiveBayes median = 0.94
----------------------------------------------------------
Comparing Test results
randomforests VS NaiveBayes --> WilcoxonResult(statistic=0.0, pvalue=7.15180158027085e-10)
randomforests median = 0.94
NaiveBayes median = 0.22
----------------------------------------------------------
Comparing CV results
randomforests VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 0.945144820219
hmm median = 0.468582065488
----------------------------------------------------------
Comparing Train results
randomforests VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 1.0
hmm median = 0.507890187807
----------------------------------------------------------
Comparing Test results
randomforests VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 0.94
hmm median = 0.456295322665
----------------------------------------------------------
Comparing CV results
randomforests VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 0.945144820219
gbrt median = 0.811905572756
----------------------------------------------------------
Comparing Test results
randomforests VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=1.0692264358478034e-09)
randomforests median = 0.94
gbrt median = 0.82
----------------------------------------------------------
Comparing CV results
randomforests VS logistic --> WilcoxonResult(statistic=332.0, pvalue=0.0031872493085119301)
randomforests median = 0.945144820219
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
randomforests VS logistic --> WilcoxonResult(statistic=0.0, pvalue=3.7283264067121769e-10)
randomforests median = 1.0
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
randomforests VS logistic --> WilcoxonResult(statistic=274.5, pvalue=0.16094827708643422)
randomforests median = 0.94
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
randomforests VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
randomforests median = 0.945144820219
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
randomforests VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2846216069602605e-10)
randomforests median = 1.0
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
randomforests VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2565634834907584e-10)
randomforests median = 0.94
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
NaiveBayes VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
NaiveBayes median = 0.25516999165
hmm median = 0.468582065488
----------------------------------------------------------
Comparing Train results
NaiveBayes VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
NaiveBayes median = 0.94
hmm median = 0.507890187807
----------------------------------------------------------
Comparing Test results
NaiveBayes VS hmm --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
NaiveBayes median = 0.22
hmm median = 0.456295322665
----------------------------------------------------------
Comparing CV results
NaiveBayes VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
NaiveBayes median = 0.25516999165
gbrt median = 0.811905572756
----------------------------------------------------------
Comparing Train results
NaiveBayes VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.2351728231245897e-10)
NaiveBayes median = 0.94
gbrt median = 1.0
----------------------------------------------------------
Comparing Test results
NaiveBayes VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.2072859016157296e-10)
NaiveBayes median = 0.22
gbrt median = 0.82
----------------------------------------------------------
Comparing CV results
NaiveBayes VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
NaiveBayes median = 0.25516999165
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
NaiveBayes VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.3460264637993095e-10)
NaiveBayes median = 0.94
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
NaiveBayes VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.1860258079624116e-10)
NaiveBayes median = 0.22
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
NaiveBayes VS adaboost --> WilcoxonResult(statistic=20.0, pvalue=2.5085309417080861e-09)
NaiveBayes median = 0.25516999165
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
NaiveBayes VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5092201842048304e-10)
NaiveBayes median = 0.94
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
NaiveBayes VS adaboost --> WilcoxonResult(statistic=37.5, pvalue=1.0490782246821493e-08)
NaiveBayes median = 0.22
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
hmm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.468582065488
gbrt median = 0.811905572756
----------------------------------------------------------
Comparing Train results
hmm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.507890187807
gbrt median = 1.0
----------------------------------------------------------
Comparing Test results
hmm VS gbrt --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.456295322665
gbrt median = 0.82
----------------------------------------------------------
Comparing CV results
hmm VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.468582065488
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
hmm VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.507890187807
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
hmm VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.456295322665
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
hmm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.468582065488
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
hmm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.507890187807
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
hmm VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
hmm median = 0.456295322665
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
gbrt VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
gbrt median = 0.811905572756
logistic median = 0.954389885699
----------------------------------------------------------
Comparing Train results
gbrt VS logistic --> WilcoxonResult(statistic=0.0, pvalue=3.7283264067121769e-10)
gbrt median = 1.0
logistic median = 0.985
----------------------------------------------------------
Comparing Test results
gbrt VS logistic --> WilcoxonResult(statistic=0.0, pvalue=7.2450383863288135e-10)
gbrt median = 0.82
logistic median = 0.96
----------------------------------------------------------
Comparing CV results
gbrt VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
gbrt median = 0.811905572756
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
gbrt VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2846216069602605e-10)
gbrt median = 1.0
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
gbrt VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2763591177255857e-10)
gbrt median = 0.82
adaboost median = 0.13
----------------------------------------------------------
Comparing CV results
logistic VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.5569294558635658e-10)
logistic median = 0.954389885699
adaboost median = 0.20407303352
----------------------------------------------------------
Comparing Train results
logistic VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2664552446107486e-10)
logistic median = 0.985
adaboost median = 0.22
----------------------------------------------------------
Comparing Test results
logistic VS adaboost --> WilcoxonResult(statistic=0.0, pvalue=7.2565634834907584e-10)
logistic median = 0.96
adaboost median = 0.13
----------------------------------------------------------