84 lines
5.4 KiB
Text
84 lines
5.4 KiB
Text
# stabilty [6]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.738854 0.698795 0.783784 0.707143 0.702498
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1 Naive Bayes 0.627451 0.607595 0.648649 0.592857 0.589476
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2 K-Nearest Neighbors 0.731707 0.666667 0.810811 0.685714 0.678133
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3 SVM 0.729412 0.645833 0.837838 0.671429 0.661343
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4 MLP 0.670968 0.641975 0.702703 0.635714 0.631654
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5 Decision Tree 0.653595 0.632911 0.675676 0.621429 0.618141
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6 Extra Trees 0.733728 0.652632 0.837838 0.678571 0.668919
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7 Random Forest 0.726190 0.648936 0.824324 0.671429 0.662162
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8 XGBoost 0.704403 0.658824 0.756757 0.664286 0.658681)
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# evolution [3]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.795181 0.717391 0.891892 0.757143 0.748976
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1 Naive Bayes 0.805031 0.752941 0.864865 0.778571 0.773342
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2 K-Nearest Neighbors 0.735484 0.703704 0.770270 0.707143 0.703317
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3 SVM 0.797619 0.712766 0.905405 0.757143 0.748157
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4 MLP 0.787879 0.714286 0.878378 0.750000 0.742219
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5 Decision Tree 0.631579 0.615385 0.648649 0.600000 0.597052
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6 Extra Trees 0.688312 0.662500 0.716216 0.657143 0.653563
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7 Random Forest 0.704403 0.658824 0.756757 0.664286 0.658681
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8 XGBoost 0.713376 0.674699 0.756757 0.678571 0.673833)
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# str features [4]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.729412 0.645833 0.837838 0.671429 0.661343
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1 Naive Bayes 0.723926 0.662921 0.797297 0.678571 0.671376
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2 K-Nearest Neighbors 0.662338 0.637500 0.689189 0.628571 0.624898
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3 SVM 0.727273 0.627451 0.864865 0.657143 0.644554
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4 MLP 0.710843 0.641304 0.797297 0.657143 0.648649
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5 Decision Tree 0.561151 0.600000 0.527027 0.564286 0.566544
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6 Extra Trees 0.567376 0.597015 0.540541 0.564286 0.565725
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7 Random Forest 0.596026 0.584416 0.608108 0.564286 0.561630
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8 XGBoost 0.630872 0.626667 0.635135 0.607143 0.605446)
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#=========================================================================
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# stability + evolution + str features [13 = 6+3+4]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.726115 0.686747 0.770270 0.692857 0.688165
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1 Naive Bayes 0.730769 0.695122 0.770270 0.700000 0.695741
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2 K-Nearest Neighbors 0.742515 0.666667 0.837838 0.692857 0.684070
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3 SVM 0.763636 0.692308 0.851351 0.721429 0.713554
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4 MLP 0.717949 0.682927 0.756757 0.685714 0.681409
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5 Decision Tree 0.671429 0.712121 0.635135 0.671429 0.673628
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6 Extra Trees 0.756410 0.719512 0.797297 0.728571 0.724406
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7 Random Forest 0.742138 0.694118 0.797297 0.707143 0.701679
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8 XGBoost 0.692810 0.670886 0.716216 0.664286 0.661138)
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# stability + evolution [9=6+3]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.729560 0.682353 0.783784 0.692857 0.687346
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1 Naive Bayes 0.743590 0.707317 0.783784 0.714286 0.710074
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2 K-Nearest Neighbors 0.720497 0.666667 0.783784 0.678571 0.672195
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3 SVM 0.771084 0.695652 0.864865 0.728571 0.720311
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4 MLP 0.679739 0.658228 0.702703 0.650000 0.646806
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5 Decision Tree 0.620690 0.633803 0.608108 0.607143 0.607084
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6 Extra Trees 0.727273 0.700000 0.756757 0.700000 0.696560
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7 Random Forest 0.734177 0.690476 0.783784 0.700000 0.694922
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8 XGBoost 0.675497 0.662338 0.689189 0.650000 0.647625)
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# stability + str features [10=6+4]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.750000 0.697674 0.810811 0.714286 0.708436
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1 Naive Bayes 0.714286 0.687500 0.743243 0.685714 0.682228
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2 K-Nearest Neighbors 0.687500 0.639535 0.743243 0.642857 0.636773
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3 SVM 0.743902 0.677778 0.824324 0.700000 0.692465
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4 MLP 0.716981 0.670588 0.770270 0.678571 0.673014
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5 Decision Tree 0.616438 0.625000 0.608108 0.600000 0.599509
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6 Extra Trees 0.697368 0.679487 0.716216 0.671429 0.668714
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7 Random Forest 0.684211 0.666667 0.702703 0.657143 0.654382
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8 XGBoost 0.666667 0.645570 0.689189 0.635714 0.632473)
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# evolution + str features[7=3+4]
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Model F1_Score Precision Recall Accuracy ROC_AUC
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0 Logistic Regression 0.773006 0.707865 0.851351 0.735714 0.728706
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1 Naive Bayes 0.750000 0.730769 0.770270 0.728571 0.726044
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2 K-Nearest Neighbors 0.737500 0.686047 0.797297 0.700000 0.694103
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3 SVM 0.763636 0.692308 0.851351 0.721429 0.713554
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4 MLP 0.775758 0.703297 0.864865 0.735714 0.727887
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5 Decision Tree 0.675497 0.662338 0.689189 0.650000 0.647625
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6 Extra Trees 0.715232 0.701299 0.729730 0.692857 0.690622
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7 Random Forest 0.715232 0.701299 0.729730 0.692857 0.690622
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8 XGBoost 0.721519 0.678571 0.770270 0.685714 0.680590)
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