Classification on Miniboone
4.84Error RateRealMLP
Evaluation results
| Method | Links | ||
|---|---|---|---|
| RealMLPaveraging=five train/test splits, tool=pytabkit2025.08 | 4.84 | — | |
| ResNet-RTDLaveraging=five train/test splits, tool=pytabkit2025.08 | 4.88 | — | |
| MLP-RTDLaveraging=five train/test splits, tool=pytabkit2025.08 | 5.03 | — | |
| MLP-PLRaveraging=five train/test splits, tool=pytabkit2025.08 | 5.04 | — | |
| LGBMaveraging=five train/test splits, tool=pytabkit2025.08 | 5.25 | — | |
| XGBaveraging=five train/test splits, tool=pytabkit2025.08 | 5.29 | — | |
| CatBoostaveraging=five train/test splits, tool=pytabkit2025.08 | 5.38 | — | |
| xRFMaveraging=five train/test splits, tool=pytabkit2025.08 | 5.39 | — | |
| FM-GPd=50, n=1200642026.05 | 6.08 | — | |
| Vard=50, n=1200642026.05 | 6.43 | — | |
| GPnnd=50, n=1200642026.05 | 6.63 | — | |
| 3-Layer NN + Dropoutn_l=20, n_u=65k, classes=2, training=labeled data only2019.12 | — | 77.3 | |
| FlowGMMn_l=20, n_u=65k, classes=2, training=semi-supervised2019.12 | — | 80.6 | |
| kNNn_l=20, n_u=65k, classes=2, training=labeled data only2019.12 | — | 77.7 | |
| kNN Label Spreadingn_l=20, n_u=65k, classes=2, training=semi-supervised2019.12 | — | 78.1 | |
| Logistic Regressionn_l=20, n_u=65k, classes=2, training=labeled data only2019.12 | — | 75.9 | |
| RBF Label Spreadingn_l=20, n_u=65k, classes=2, training=semi-supervised2019.12 | — | 78.8 | |
| Π-modeln_l=20, n_u=65k, classes=2, training=semi-supervised2019.12 | — | 78.3 |