Classification on German
79.4AccuracyChunked TabPFN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Chunked TabPFNScenario=Non-strategic2026.05 | 79.4 | — | |
| TabPFN v2.5Scenario=Non-strategic2026.05 | 79.2 | — | |
| Drift-Resilient TabPFNScenario=Non-strategic2026.05 | 78.9 | — | |
| TabICLScenario=Non-strategic2026.05 | 78.9 | — | |
| TabFlexScenario=Non-strategic2026.05 | 78.62 | — | |
| MLPScenario=Non-strategic2026.05 | 78.02 | — | |
| TabDPTScenario=Non-strategic2026.05 | 77.93 | — | |
| SPNScenario=Non-strategic2026.05 | 77.9 | — | |
| XGBoostScenario=Non-strategic2026.05 | 77.8 | — | |
| LightGBMScenario=Non-strategic2026.05 | 77.73 | — | |
| SPNScenario=Strategic2026.05 | 77.71 | — | |
| CatBoostScenario=Non-strategic2026.05 | 77.6 | — | |
| Linear modelsScenario=Non-strategic2026.05 | 76.24 | — | |
| SVMScenario=Non-strategic2026.05 | 75.86 | — | |
| LightGBMScenario=Strategic2026.05 | 75.39 | — | |
| TabDPTScenario=Strategic2026.05 | 74.82 | — | |
| MLPScenario=Strategic2026.05 | 74.63 | — | |
| TabFlexScenario=Strategic2026.05 | 74.27 | — | |
| XGBoostScenario=Strategic2026.05 | 74.11 | — | |
| Drift-Resilient TabPFNScenario=Strategic2026.05 | 73.9 | — | |
| CatBoostScenario=Strategic2026.05 | 73.72 | — | |
| Chunked TabPFNScenario=Strategic2026.05 | 73.4 | — | |
| TabPFN v2.5Scenario=Strategic2026.05 | 73.05 | — | |
| Linear modelsScenario=Strategic2026.05 | 72.91 | — | |
| TabICLScenario=Strategic2026.05 | 72.83 | — | |
| PeerPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 72.7 | — | |
| SVMScenario=Strategic2026.05 | 72.54 | — | |
| SymmPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 70.9 | — | |
| Peernoise_rates=0.1, 0.3, prior_equalization=p = 0.52019.10 | 70.1 | — | |
| SurrPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 68.1 | — | |
| Peernoise_rates=0.1, 0.3, prior_equalization=p != 0.52019.10 | 68 | — | |
| PeerPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 67.6 | — | |
| DMIPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 66.6 | — | |
| PeerPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 66.4 | — | |
| PeerPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 65.4 | — | |
| NNPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 64.8 | — | |
| SurrPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 64.5 | — | |
| PeerPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 63.9 | — | |
| SurrPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 63.2 | — | |
| DMIPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 61.8 | — | |
| DMIPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 61.1 | — | |
| PeerPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 60.6 | — | |
| SymmPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 60 | — | |
| SurrPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 59 | — | |
| DMIPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 57.3 | — | |
| SymmPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 57.3 | — | |
| DMIPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 57.3 | — | |
| NNPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 57.2 | — | |
| SurrPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 56.3 | — | |
| NNPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 55.6 | — | |
| NNPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 55.3 | — | |
| SurrPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 55 | — | |
| SymmPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.4, 0.42019.10 | 54.9 | — | |
| SymmPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 53.7 | — | |
| NNPrior Equalization=p != 0.5, Noise rates (e-1, e+1)=0.2, 0.42019.10 | 53.5 | — | |
| DMIPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 52.9 | — | |
| NNPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 51.9 | — | |
| SymmPrior Equalization=p = 0.5, Noise rates (e-1, e+1)=0.1, 0.32019.10 | 50.7 | — | |
| AUESamples per task=1502023.10 | — | 29 | |
| AUESamples per task=1002023.10 | — | 32 | |
| CondorSamples per task=1502023.10 | — | 31 | |
| CondorSamples per task=1002023.10 | — | 30 | |
| DriftSurfSamples per task=1502023.10 | — | 29 | |
| DriftSurfSamples per task=1002023.10 | — | 29 | |
| ELLASamples per task=1502023.10 | — | 25 | |
| ELLASamples per task=1002023.10 | — | 29 | |
| EWCSamples per task=1502023.10 | — | 30 | |
| EWCSamples per task=1002023.10 | — | 30 | |
| GEMSamples per task=1502023.10 | — | 34 | |
| GEMSamples per task=1002023.10 | — | 34 | |
| IMRCSamples per task=1502023.10 | — | 28 | |
| IMRCSamples per task=1002023.10 | — | 29 | |
| MERSamples per task=1502023.10 | — | 30 | |
| MERSamples per task=1002023.10 | — | 29 |