Classification on Cardiac Phenotyping 5-fold CV (test)
81AccuracyCW-B
Evaluation Results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| CW-BValidation Protocol=5-fold stratified cross-validation, Boosting iterations=600, Depth=5, Learning rate=0.07, Subsampling=0.95, l2=1.02026.06 | 81 | 72 | 73 | 67 | 69 | 33 | 0.0103 | |
| XGBValidation Protocol=5-fold stratified cross-validation, Boosting iterations=1200, Depth=7, Learning rate=0.03, Subsampling=0.95, l2=1.02026.06 | 80 | 69 | 66 | 65 | 67 | 35 | 0.0108 | |
| CBValidation Protocol=5-fold stratified cross-validation, Boosting iterations=1200, Depth=8, Learning rate=0.032026.06 | 78 | 70 | 70 | 66 | 66 | 34 | 0.0136 | |
| BCValidation Protocol=5-fold stratified cross-validation, Architecture=MLP(256), Optimizer=Adam, Learning rate=10−32026.06 | 77 | 68 | 67 | 62 | 64 | 38 | 0.0109 | |
| DQNValidation Protocol=5-fold stratified cross-validation, Architecture=MLP(256), Optimizer=Adam, Learning rate=10−32026.06 | 76 | 68 | 68 | 66 | 64 | 34 | 0.007 | |
| MLPValidation Protocol=5-fold stratified cross-validation, Architecture=MLP(256,128), Features=early stopping, Optimizer=Adam, Learning rate=10−32026.06 | 76 | 65 | 63 | 58 | 60 | 42 | 0.018 | |
| STKValidation Protocol=5-fold stratified cross-validation, Base learners=CatBoost and XGBoost, Meta learner=logistic regression, Training mode=out of fold training2026.06 | 75 | 69 | 71 | 69 | 66 | 31 | 0.0069 |