psqi_lcga_group_2
HRV_SDNN_MS
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_SDNN_MS']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.712 0.506 0.449 0.579 0.796 0.840 0.757 0.311 0.711 9.800 20.000
NaiveBayes 0.706 0.462 0.556 0.395 0.850 0.811 0.892 0.324 0.765 5.400 24.400
KNN 0.677 0.367 0.500 0.289 0.840 0.787 0.901 0.234 0.745 4.400 25.400
SVM 0.662 0.512 0.458 0.579 0.802 0.842 0.766 0.322 0.718 9.600 20.200
RandomForest 0.657 0.431 0.519 0.368 0.841 0.803 0.883 0.284 0.752 5.400 24.400
DecisionTree 0.647 0.474 0.474 0.474 0.820 0.820 0.820 0.294 0.732 7.600 22.200
MLP 0.611 0.400 0.438 0.368 0.816 0.795 0.838 0.219 0.718 6.400 23.400
XGBoost 0.608 0.405 0.390 0.421 0.785 0.796 0.775 0.191 0.685 8.200 21.600
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 84 27 16 22
NaiveBayes 99 12 23 15
KNN 100 11 27 11
SVM 85 26 16 22
RandomForest 98 13 24 14
DecisionTree 91 20 20 18
MLP 93 18 24 14
XGBoost 86 25 22 16
HRV_NN50
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_NN50']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
NaiveBayes 0.741 0.469 0.577 0.395 0.855 0.813 0.901 0.340 0.772 5.200 24.600
LogisticRegression 0.736 0.529 0.469 0.605 0.806 0.850 0.766 0.344 0.725 9.800 20.000
SVM 0.713 0.469 0.383 0.605 0.740 0.831 0.667 0.242 0.651 12.000 17.800
KNN 0.681 0.406 0.500 0.342 0.838 0.797 0.883 0.258 0.745 5.200 24.600
RandomForest 0.651 0.419 0.542 0.342 0.847 0.800 0.901 0.288 0.758 4.800 25.000
DecisionTree 0.616 0.427 0.432 0.421 0.807 0.804 0.811 0.234 0.711 7.400 22.400
XGBoost 0.597 0.451 0.485 0.421 0.828 0.810 0.847 0.281 0.738 6.600 23.200
MLP 0.533 0.412 0.467 0.368 0.826 0.798 0.856 0.244 0.732 6.000 23.800
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
NaiveBayes 100 11 23 15
LogisticRegression 85 26 15 23
SVM 74 37 15 23
KNN 98 13 25 13
RandomForest 100 11 25 13
DecisionTree 90 21 22 16
XGBoost 94 17 22 16
MLP 95 16 24 14
HRV_PNN50
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_PNN50']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.764 0.556 0.481 0.658 0.808 0.866 0.757 0.379 0.732 10.400 19.400
NaiveBayes 0.753 0.476 0.600 0.395 0.860 0.815 0.910 0.355 0.779 5.000 24.800
KNN 0.702 0.375 0.462 0.316 0.829 0.789 0.874 0.218 0.732 5.200 24.600
SVM 0.661 0.467 0.404 0.553 0.769 0.825 0.721 0.250 0.678 10.400 19.400
RandomForest 0.627 0.369 0.444 0.316 0.824 0.787 0.865 0.204 0.725 5.400 24.400
MLP 0.614 0.371 0.406 0.342 0.807 0.786 0.829 0.181 0.705 6.400 23.400
DecisionTree 0.602 0.415 0.386 0.447 0.778 0.800 0.757 0.195 0.678 8.800 21.000
XGBoost 0.598 0.410 0.400 0.421 0.791 0.798 0.784 0.201 0.691 8.000 21.800
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 84 27 13 25
NaiveBayes 101 10 23 15
KNN 97 14 26 12
SVM 80 31 17 21
RandomForest 96 15 26 12
MLP 92 19 25 13
DecisionTree 84 27 21 17
XGBoost 87 24 22 16
HRV_RMSSD_MS
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_RMSSD_MS']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
NaiveBayes 0.752 0.469 0.577 0.395 0.855 0.813 0.901 0.340 0.772 5.200 24.600
LogisticRegression 0.732 0.549 0.472 0.658 0.802 0.865 0.748 0.369 0.725 10.600 19.200
KNN 0.699 0.400 0.481 0.342 0.833 0.795 0.874 0.244 0.738 5.400 24.400
SVM 0.693 0.511 0.429 0.632 0.775 0.849 0.712 0.309 0.691 11.200 18.600
MLP 0.650 0.432 0.444 0.421 0.812 0.805 0.820 0.245 0.718 7.200 22.600
XGBoost 0.642 0.442 0.436 0.447 0.805 0.809 0.802 0.247 0.711 7.800 22.000
DecisionTree 0.637 0.463 0.432 0.500 0.796 0.819 0.775 0.263 0.705 8.800 21.000
RandomForest 0.636 0.424 0.500 0.368 0.836 0.802 0.874 0.270 0.745 5.600 24.200
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
NaiveBayes 100 11 23 15
LogisticRegression 83 28 13 25
KNN 97 14 25 13
SVM 79 32 14 24
MLP 91 20 22 16
XGBoost 89 22 21 17
DecisionTree 86 25 19 19
RandomForest 97 14 24 14
HRV_VLF
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_VLF']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
NaiveBayes 0.718 0.469 0.577 0.395 0.855 0.813 0.901 0.340 0.772 5.200 24.600
LogisticRegression 0.709 0.524 0.478 0.579 0.813 0.845 0.784 0.342 0.732 9.200 20.600
KNN 0.679 0.448 0.650 0.342 0.867 0.806 0.937 0.357 0.785 4.000 25.800
SVM 0.652 0.452 0.413 0.500 0.785 0.816 0.757 0.242 0.691 9.200 20.600
RandomForest 0.646 0.387 0.500 0.316 0.839 0.792 0.892 0.246 0.745 4.800 25.000
XGBoost 0.600 0.394 0.424 0.368 0.811 0.793 0.829 0.207 0.711 6.600 23.200
MLP 0.560 0.416 0.410 0.421 0.796 0.800 0.793 0.212 0.698 7.800 22.000
DecisionTree 0.558 0.342 0.342 0.342 0.775 0.775 0.775 0.117 0.664 7.600 22.200
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
NaiveBayes 100 11 23 15
LogisticRegression 87 24 16 22
KNN 104 7 25 13
SVM 84 27 19 19
RandomForest 99 12 26 12
XGBoost 92 19 24 14
MLP 88 23 22 16
DecisionTree 86 25 25 13
HRV_LF
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_LF']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
NaiveBayes 0.743 0.485 0.571 0.421 0.853 0.818 0.892 0.349 0.772 5.600 24.200
LogisticRegression 0.742 0.543 0.463 0.658 0.796 0.863 0.739 0.360 0.718 10.800 19.000
SVM 0.718 0.568 0.500 0.658 0.819 0.869 0.775 0.399 0.745 10.000 19.800
KNN 0.705 0.351 0.526 0.263 0.846 0.785 0.919 0.238 0.752 3.800 26.000
RandomForest 0.687 0.400 0.438 0.368 0.816 0.795 0.838 0.219 0.718 6.400 23.400
MLP 0.645 0.405 0.417 0.395 0.804 0.796 0.811 0.209 0.705 7.200 22.600
XGBoost 0.637 0.427 0.432 0.421 0.807 0.804 0.811 0.234 0.711 7.400 22.400
DecisionTree 0.598 0.405 0.390 0.421 0.785 0.796 0.775 0.191 0.685 8.200 21.600
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
NaiveBayes 99 12 22 16
LogisticRegression 82 29 13 25
SVM 86 25 13 25
KNN 102 9 28 10
RandomForest 93 18 24 14
MLP 90 21 23 15
XGBoost 90 21 22 16
DecisionTree 86 25 22 16
HRV_HF
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_HF']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
KNN 0.742 0.406 0.500 0.342 0.838 0.797 0.883 0.258 0.745 5.200 24.600
NaiveBayes 0.733 0.476 0.600 0.395 0.860 0.815 0.910 0.355 0.779 5.000 24.800
LogisticRegression 0.718 0.545 0.480 0.632 0.810 0.859 0.766 0.367 0.732 10.000 19.800
SVM 0.695 0.512 0.477 0.553 0.815 0.838 0.793 0.330 0.732 8.800 21.000
XGBoost 0.672 0.427 0.432 0.421 0.807 0.804 0.811 0.234 0.711 7.400 22.400
RandomForest 0.668 0.364 0.429 0.316 0.819 0.785 0.856 0.192 0.718 5.600 24.200
MLP 0.661 0.394 0.424 0.368 0.811 0.793 0.829 0.207 0.711 6.600 23.200
DecisionTree 0.598 0.400 0.405 0.395 0.798 0.795 0.802 0.198 0.698 7.400 22.400
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
KNN 98 13 25 13
NaiveBayes 101 10 23 15
LogisticRegression 85 26 14 24
SVM 88 23 17 21
XGBoost 90 21 22 16
RandomForest 95 16 26 12
MLP 92 19 24 14
DecisionTree 89 22 23 15
HRV_LF_HF
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_LF_HF']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.774 0.607 0.529 0.711 0.833 0.888 0.784 0.454 0.765 10.200 19.600
NaiveBayes 0.773 0.522 0.581 0.474 0.856 0.831 0.883 0.383 0.779 6.200 23.600
SVM 0.745 0.612 0.553 0.684 0.845 0.882 0.811 0.464 0.779 9.400 20.400
KNN 0.721 0.448 0.517 0.395 0.840 0.808 0.874 0.296 0.752 5.800 24.000
RandomForest 0.707 0.455 0.536 0.395 0.845 0.810 0.883 0.310 0.758 5.600 24.200
XGBoost 0.668 0.507 0.514 0.500 0.834 0.830 0.838 0.341 0.752 7.400 22.400
DecisionTree 0.616 0.427 0.432 0.421 0.807 0.804 0.811 0.234 0.711 7.400 22.400
MLP 0.611 0.453 0.459 0.447 0.816 0.812 0.820 0.270 0.725 7.400 22.400
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 87 24 11 27
NaiveBayes 98 13 20 18
SVM 90 21 12 26
KNN 97 14 23 15
RandomForest 98 13 23 15
XGBoost 93 18 19 19
DecisionTree 90 21 22 16
MLP 91 20 21 17
HRV_POWER
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_POWER']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.723 0.523 0.460 0.605 0.800 0.848 0.757 0.334 0.718 10.000 19.800
NaiveBayes 0.709 0.438 0.538 0.368 0.846 0.805 0.892 0.299 0.758 5.200 24.600
RandomForest 0.673 0.455 0.536 0.395 0.845 0.810 0.883 0.310 0.758 5.600 24.200
SVM 0.662 0.483 0.429 0.553 0.787 0.830 0.748 0.279 0.698 9.800 20.000
KNN 0.649 0.414 0.600 0.316 0.858 0.798 0.928 0.312 0.772 4.000 25.800
DecisionTree 0.637 0.463 0.432 0.500 0.796 0.819 0.775 0.263 0.705 8.800 21.000
XGBoost 0.632 0.410 0.400 0.421 0.791 0.798 0.784 0.201 0.691 8.000 21.800
MLP 0.598 0.347 0.351 0.342 0.780 0.777 0.784 0.127 0.671 7.400 22.400
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 84 27 15 23
NaiveBayes 99 12 24 14
RandomForest 98 13 23 15
SVM 83 28 17 21
KNN 103 8 26 12
DecisionTree 86 25 19 19
XGBoost 87 24 22 16
MLP 87 24 25 13
HRV_EKG_HR
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_EKG_HR']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
NaiveBayes 0.706 0.492 0.593 0.421 0.858 0.820 0.901 0.364 0.779 5.400 24.400
KNN 0.691 0.400 0.481 0.342 0.833 0.795 0.874 0.244 0.738 5.400 24.400
LogisticRegression 0.685 0.455 0.400 0.526 0.771 0.818 0.730 0.236 0.678 10.000 19.800
SVM 0.667 0.554 0.511 0.605 0.828 0.856 0.802 0.386 0.752 9.000 20.800
RandomForest 0.652 0.435 0.484 0.395 0.830 0.805 0.856 0.269 0.738 6.200 23.600
MLP 0.615 0.395 0.395 0.395 0.793 0.793 0.793 0.188 0.691 7.600 22.200
DecisionTree 0.602 0.410 0.400 0.421 0.791 0.798 0.784 0.201 0.691 8.000 21.800
XGBoost 0.588 0.462 0.450 0.474 0.809 0.817 0.802 0.271 0.718 8.000 21.800
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
NaiveBayes 100 11 22 16
KNN 97 14 25 13
LogisticRegression 81 30 18 20
SVM 89 22 15 23
RandomForest 95 16 23 15
MLP 88 23 23 15
DecisionTree 87 24 22 16
XGBoost 89 22 20 18
HRV_EKG_HR_MAXMIN
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_EKG_HR_MAXMIN']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.701 0.523 0.460 0.605 0.800 0.848 0.757 0.334 0.718 10.000 19.800
NaiveBayes 0.697 0.444 0.560 0.368 0.851 0.806 0.901 0.314 0.765 5.000 24.800
SVM 0.639 0.506 0.467 0.553 0.809 0.837 0.784 0.319 0.725 9.000 20.800
RandomForest 0.593 0.406 0.500 0.342 0.838 0.797 0.883 0.258 0.745 5.200 24.600
KNN 0.577 0.276 0.400 0.211 0.825 0.767 0.892 0.131 0.718 4.000 25.800
XGBoost 0.527 0.329 0.317 0.342 0.758 0.769 0.748 0.088 0.644 8.200 21.600
DecisionTree 0.513 0.302 0.271 0.342 0.717 0.752 0.685 0.025 0.597 9.600 20.200
MLP 0.485 0.353 0.319 0.395 0.742 0.775 0.712 0.100 0.631 9.400 20.400
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 84 27 15 23
NaiveBayes 100 11 24 14
SVM 87 24 17 21
RandomForest 98 13 25 13
KNN 99 12 30 8
XGBoost 83 28 25 13
DecisionTree 76 35 25 13
MLP 79 32 23 15
HRV_RESP_RATE
[資料] 來源=isi_raw_data 目標=psqi_lcga_group_2 列數=149 特徵數=3
[特徵] 使用欄位（前 15）：['BDI_T1', 'BAI_T1', 'HRV_RESP_RATE']
[CV] Stratified 5-fold, seed=42 | class_weight=balanced
[聚合] K-fold = weighted | LOSO = weighted
[Leakage check] Class balance
count percent%
psqi_lcga_group_2
0 111 74.5
1 38 25.5
[Leakage check] 未發現與目標 |r| ≥ 0.95 的欄位。
=== Basic ML Benchmark (Stratified 5-fold CV) ===
model AUC F1_pos(=1) Prec_pos Rec_pos F1_neg(=0) Prec_neg Rec_neg MCC Accuracy Pred1_mean Pred0_mean
LogisticRegression 0.763 0.552 0.490 0.632 0.815 0.860 0.775 0.377 0.738 9.800 20.000
NaiveBayes 0.746 0.500 0.615 0.421 0.863 0.821 0.910 0.380 0.785 5.200 24.600
SVM 0.733 0.541 0.489 0.605 0.817 0.853 0.784 0.365 0.738 9.400 20.400
KNN 0.698 0.492 0.593 0.421 0.858 0.820 0.901 0.364 0.779 5.400 24.400
RandomForest 0.661 0.478 0.552 0.421 0.848 0.817 0.883 0.335 0.765 5.800 24.000
DecisionTree 0.633 0.456 0.439 0.474 0.804 0.815 0.793 0.260 0.711 8.200 21.600
XGBoost 0.626 0.487 0.475 0.500 0.818 0.826 0.811 0.306 0.732 8.000 21.800
MLP 0.535 0.400 0.438 0.368 0.816 0.795 0.838 0.219 0.718 6.400 23.400
--- Aggregated Confusion Matrix Sums (across all folds' test parts) ---
model TN_sum FP_sum FN_sum TP_sum
LogisticRegression 86 25 14 24
NaiveBayes 101 10 22 16
SVM 87 24 15 23
KNN 100 11 22 16
RandomForest 98 13 22 16
DecisionTree 88 23 20 18
XGBoost 90 21 19 19
MLP 93 18 24 14