Classification on Arcene
90.6AccuracyGOTabPFN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| GOTabPFNEvaluation Protocol=5 x 5 CV2026.06 | 90.6 | 1 | |
| TabPFN-WEvaluation Protocol=5 x 5 CV2026.06 | 88 | 3.75 | |
| TANDEMEvaluation Protocol=5 x 5 CV2026.06 | 86.9 | 3.63 | |
| BETAEvaluation Protocol=5 x 5 CV2026.06 | 86.45 | 8.13 | |
| DynaTab2026.05 | 83 | — | |
| DynaTab2026.05 | 83 | — | |
| TABICLEvaluation Protocol=5 x 5 CV2026.06 | 82.6 | 7.63 | |
| KNN2026.05 | 82.5 | — | |
| TABDPTEvaluation Protocol=5 x 5 CV2026.06 | 82.1 | 4.88 | |
| GBM2026.05 | 82 | — | |
| GRIFFINBackbone=Gemma 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 81.99 | — | |
| I-GLASSBackbone=Gemma 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 81.69 | — | |
| ProtoGate2026.05 | 81.5 | — | |
| TabulaRNN2026.05 | 81.5 | — | |
| XGBoost2026.05 | 81.5 | — | |
| ProtoGateSubsampling=11K2026.05 | 81.5 | — | |
| TabulaRNN2026.05 | 81.5 | — | |
| PROTOGATEEvaluation Protocol=5 x 5 CV2026.06 | 81.5 | 12.06 | |
| TTABLESEvaluation Protocol=5 x 5 CV2026.06 | 81.4 | 8.38 | |
| Lasso2026.05 | 81 | — | |
| CatBoost2026.05 | 81 | — | |
| Lasso2026.05 | 81 | — | |
| LASSOEvaluation Protocol=5 x 5 CV2026.06 | 81 | 11.13 | |
| LLSPIN2026.05 | 80.8 | — | |
| LGBM2026.05 | 80.5 | — | |
| LGBM2026.05 | 80.5 | — | |
| I-GLASSBackbone=Mistral 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 79.76 | — | |
| GRIFFINBackbone=Mistral 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 79.71 | — | |
| LSPIN2026.05 | 78.6 | — | |
| MLP2026.05 | 78.4 | — | |
| MLP2026.05 | 78.4 | — | |
| MLPEvaluation Protocol=5 x 5 CV2026.06 | 78.4 | 11.63 | |
| REAL-X2026.05 | 77.3 | — | |
| SVM2026.05 | 77 | — | |
| I-GLASSBackbone=Llama2 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 76.3 | — | |
| GRIFFINBackbone=Llama2 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 76.3 | — | |
| TabR2026.05 | 75.85 | — | |
| AdaBoost2026.05 | 75.5 | — | |
| I-GLASSBackbone=ReLU-Llama2 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 74.62 | — | |
| GRIFFINBackbone=ReLU-Llama2 7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 74.62 | — | |
| STG2026.05 | 74.4 | — | |
| Random Forest2026.05 | 74 | — | |
| Decision Tree2026.05 | 73.5 | — | |
| L2X2026.05 | 72.95 | — | |
| INVASE2026.05 | 71.2 | — | |
| Mambular2026.05 | 69.65 | — | |
| AutoInt2026.05 | 67.9 | — | |
| TabM2026.05 | 66.1 | — | |
| TabSeq2026.05 | 65.3 | — | |
| MambaTab2026.05 | 64 | — | |
| DeepFM2026.05 | 64 | — | |
| I-GLASSBackbone=OPT 6.7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 63.93 | — | |
| GRIFFINBackbone=OPT 6.7B, Sparsity=50% FF, Evaluation Protocol=0-shot unnormalized2025.08 | 63.89 | — | |
| DCN2026.05 | 61.75 | — | |
| NODE2026.05 | 59.4 | — | |
| SAINT2026.05 | 57.1 | — | |
| CategoryEmbedding2026.05 | 54.8 | — | |
| 1-D CNN2026.05 | 54.8 | — | |
| Naive Bayes2026.05 | 53.5 | — | |
| FT-Transformer2026.05 | 52.3 | — | |
| TabNet2026.05 | 50 | — | |
| MambAttention2026.05 | 49.9 | — | |
| TabTransformer2026.05 | 48.2 | — | |
| ResNetTabular2026.05 | 42.6 | — | |
| Trompt2026.05 | 40.1 | — | |
| NDTF2026.05 | 37.7 | — | |
| ENODE2026.05 | 35.2 | — | |
| ModernNCA2026.05 | 32.8 | — | |
| DANets2026.05 | 30.3 | — | |
| TANGOS2026.05 | 27.9 | — |