Classification on Hayes-Roth LDLSS (Accuracy)
84.29AccuracyDynaTab
Evaluation results
| Method | Links | |
|---|---|---|
| DynaTab2026.05 | 84.29 | |
| XGBoost2026.05 | 83.33 | |
| Decision Tree2026.05 | 83.33 | |
| ResNetTabular2026.05 | 83.3 | |
| AutoInt2026.05 | 82.59 | |
| Random Forest2026.05 | 82.59 | |
| GBM2026.05 | 82.56 | |
| Trompt2026.05 | 82.54 | |
| TabPFN2026.05 | 81.85 | |
| TabPFN v22026.05 | 81 | |
| TabulaRNN2026.05 | 80.97 | |
| CatBoost2026.05 | 80.26 | |
| MambAttention2026.05 | 79.57 | |
| SVM2026.05 | 78.01 | |
| LSPIN2026.05 | 77.98 | |
| FT-Transformer2026.05 | 77.27 | |
| INVASE2026.05 | 75.78 | |
| LLSPIN2026.05 | 72.82 | |
| CategoryEmbedding2026.05 | 69.62 | |
| ModernNCA2026.05 | 68.65 | |
| LGBM2026.05 | 67.38 | |
| Naive Bayes2026.05 | 66.75 | |
| TabSeq2026.05 | 65.85 | |
| MambaTab2026.05 | 65.16 | |
| NODE2026.05 | 65.07 | |
| STG2026.05 | 64.43 | |
| NDTF2026.05 | 63.67 | |
| 1-D CNN2026.05 | 62.82 | |
| TabM2026.05 | 62.62 | |
| DeepFM2026.05 | 62.59 | |
| DANets2026.05 | 62.59 | |
| TabNet2026.05 | 62.58 | |
| REAL-X2026.05 | 62.56 | |
| TabTransformer2026.05 | 61.09 | |
| TabR2026.05 | 60.51 | |
| L2X2026.05 | 60.49 | |
| TANGOS2026.05 | 60.43 | |
| ENODE2026.05 | 59.38 | |
| MLP2026.05 | 58.93 | |
| Lasso2026.05 | 58.91 | |
| Mambular2026.05 | 58.26 | |
| SAINT2026.05 | 58.2 | |
| DCN2026.05 | 57.41 | |
| ProtoGate2026.05 | 55.15 | |
| AdaBoost2026.05 | 48.37 | |
| KNN2026.05 | 47.72 |