Classification on Adult (ACC)
91.3AccuracyChunked TabPFN
Evaluation Results
| Method | Links | |
|---|---|---|
| Chunked TabPFNScenario=Non-strategic2026.05 | 91.3 | |
| TabPFN v2.5Scenario=Non-strategic2026.05 | 91 | |
| TabICLScenario=Non-strategic2026.05 | 90.7 | |
| Drift-Resilient TabPFNScenario=Non-strategic2026.05 | 90.6 | |
| TabDPTScenario=Non-strategic2026.05 | 89.91 | |
| SPNScenario=Non-strategic2026.05 | 89.9 | |
| XGBoostScenario=Non-strategic2026.05 | 89.8 | |
| SPNScenario=Strategic2026.05 | 89.8 | |
| CatBoostScenario=Non-strategic2026.05 | 89.6 | |
| TabFlexScenario=Non-strategic2026.05 | 88.73 | |
| LightGBMScenario=Non-strategic2026.05 | 88.44 | |
| TabR2026.05 | 87.5 | |
| CatBoost2026.05 | 87.32 | |
| LGBM2026.05 | 87.21 | |
| XGBoost2026.05 | 86.99 | |
| MLPScenario=Non-strategic2026.05 | 86.82 | |
| TabM2026.05 | 86.8 | |
| GBM2026.05 | 86.76 | |
| Drift-Resilient TabPFNScenario=Strategic2026.05 | 85.9 | |
| Random Forest2026.05 | 85.74 | |
| Chunked TabPFNScenario=Strategic2026.05 | 85.4 | |
| AdaBoost2026.05 | 85.3 | |
| Linear modelsScenario=Non-strategic2026.05 | 85.27 | |
| Multilinear-CovJactau=0.12026.05 | 85.13 | |
| SVM2026.05 | 85.12 | |
| Multilinear-STE2026.05 | 85.11 | |
| CatBoostScenario=Strategic2026.05 | 85.11 | |
| TabPFN v2.5Scenario=Strategic2026.05 | 85.05 | |
| Soft-Mix2026.05 | 85.01 | |
| Gumbel-ST2026.05 | 85.01 | |
| TabTransformer2026.05 | 85 | |
| TabSeq2026.05 | 84.98 | |
| M-CovJac#p/n=4, L=6, k=4k, iterations=50k, τ=0.12026.05 | 84.93 | |
| M-STE#p/n=4, L=6, k=4k, iterations=50k, τ=1.02026.05 | 84.91 | |
| SVMScenario=Non-strategic2026.05 | 84.91 | |
| MLP2026.05 | 84.87 | |
| TabICLScenario=Strategic2026.05 | 84.83 | |
| TabNet2026.05 | 84.8 | |
| Gumbel-ST#p/n=16, L=6, k=4k, iterations=50k, τ=1.02026.05 | 84.69 | |
| Soft-Mix#p/n=16, L=6, k=4k, iterations=50k, τ=1.02026.05 | 84.68 | |
| LightGBMScenario=Strategic2026.05 | 84.58 | |
| MLPScenario=Strategic2026.05 | 84.13 | |
| Lasso2026.05 | 83.65 | |
| XGBoostScenario=Strategic2026.05 | 83.62 | |
| CategoryEmbedding2026.05 | 83.4 | |
| DynaTab2026.05 | 83.26 | |
| TabDPTScenario=Strategic2026.05 | 83.22 | |
| KNN2026.05 | 83.07 | |
| FT-Transformer2026.05 | 83 | |
| TabFlexScenario=Strategic2026.05 | 82.96 | |
| Linear modelsScenario=Strategic2026.05 | 82.63 | |
| SAINT2026.05 | 82.6 | |
| SVMScenario=Strategic2026.05 | 82.45 | |
| CAAFEMethod Category=LLM-based Methods, Downstream Classifier=XGBoost2026.04 | 82.2 | |
| MALMASMethod Category=MALMAS, Downstream Classifier=XGBoost2026.04 | 82.2 | |
| ModernNCA2026.05 | 82.2 | |
| Mambular2026.05 | 81.8 | |
| LLMFEMethod Category=LLM-based Methods, Downstream Classifier=XGBoost2026.04 | 81.6 | |
| BaseMethod Category=Base, Downstream Classifier=XGBoost2026.04 | 81.4 | |
| AutoFeatMethod Category=Traditional Methods, Downstream Classifier=XGBoost2026.04 | 81.4 | |
| OpenFEMethod Category=Traditional Methods, Downstream Classifier=XGBoost2026.04 | 81.4 | |
| Decision Tree2026.05 | 81.38 | |
| Trompt2026.05 | 81.3 | |
| Naive Bayes2026.05 | 81.16 | |
| DFSMethod Category=Traditional Methods, Downstream Classifier=XGBoost2026.04 | 81 | |
| ResNetTabular2026.05 | 80.8 | |
| OCTreeMethod Category=LLM-based Methods, Downstream Classifier=XGBoost2026.04 | 80.3 | |
| AutoInt2026.05 | 80.2 | |
| MambaTab2026.05 | 79.6 | |
| LLSPIN2026.05 | 79.6 | |
| NODE2026.05 | 78.3 | |
| LSPIN2026.05 | 78.3 | |
| DCN2026.05 | 76.8 | |
| TabulaRNN2026.05 | 75.2 | |
| MambAttention2026.05 | 74.3 | |
| TANGOS2026.05 | 74.3 | |
| DANets2026.05 | 71.5 | |
| DeepFM2026.05 | 70.4 | |
| 1-D CNN2026.05 | 69.2 | |
| NDTF2026.05 | 56.52 | |
| STG2026.05 | 56.52 | |
| ENODE2026.05 | 46.92 | |
| L2X2026.05 | 46.92 | |
| REAL-X2026.05 | 36.48 | |
| INVASE2026.05 | 34.68 | |
| ProtoGate2026.05 | 34.56 |