Tabular Classification on TabZilla avg across 98 datasets
86Mean AccuracyTuneTables
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| TuneTables2024.02 | 86 | 7.435 | 0.494 | 0.624 | 0.49 | 30 | |
| CatBoost2024.02 | 85.6 | 7.514 | 0.496 | 0.669 | 0.566 | 13 | |
| XGBoost2024.02 | 85.4 | 7.991 | 0.411 | 0.783 | 0.533 | 16 | |
| ExcelFormer2024.02 | 84.7 | 9.349 | 0.212 | 0.863 | 0.384 | 7 | |
| LightGBM2024.02 | 84.5 | 9.122 | 0.284 | 0.894 | 0.431 | 20 | |
| RandomForest2024.02 | 84.3 | 9.713 | 0.206 | 0.776 | 0.374 | 4 | |
| SAINT2024.02 | 84 | 9.619 | 0.132 | 0.985 | 0.337 | 10 | |
| DANet2024.02 | 84 | 9.646 | 0.209 | 0.763 | 0.345 | 3 | |
| rtdl_ResNet2024.02 | 83.9 | 9.691 | 0.175 | 0.798 | 0.356 | 5 | |
| rtdl_FTTransformer2024.02 | 83.8 | 9.782 | 0.198 | 0.786 | 0.273 | 8 | |
| NODE2024.02 | 83.8 | 9.335 | 0.224 | 0.695 | 0.357 | 3 | |
| SVM2024.02 | 83.5 | 9.847 | 0.121 | 0.812 | 0.318 | 12 | |
| DecisionTree2024.02 | 82.3 | 12.192 | -0.308 | 1.215 | -0.038 | 4 | |
| rtdl_MLP2024.02 | 81.3 | 10.825 | -0.019 | 0.907 | 0.223 | 1 | |
| STG2024.02 | 81 | 12.196 | -0.29 | 1.051 | -0.057 | 5 | |
| TabNet2024.02 | 80.4 | 11.643 | -0.216 | 1.104 | 0.075 | 5 | |
| MLP2024.02 | 80.2 | 12.22 | -0.232 | 0.845 | -0.105 | 1 | |
| LinearModel2024.02 | 79.3 | 13.069 | -0.52 | 1.233 | -0.345 | 5 | |
| KNN2024.02 | 78.1 | 14.101 | -0.727 | 1.187 | -0.608 | 0 | |
| VIME2024.02 | 75.6 | 14.711 | -0.849 | 1.192 | -0.694 | 5 |