Regression benchmarks strict intersection with CNN-1D
0.002p-valueTabPFN-opt vs PLS
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| TabPFN-opt vs PLS2026.05 | 0.002 | — | — | |
| TabPFN-opt vs TabPFN-Raw2026.05 | 0.006 | — | — | |
| TabPFN-opt vs CatBoost2026.05 | 0.0218 | — | — | |
| TabPFN-opt vs Ridge2026.05 | 0.148 | — | — | |
| Ridge vs CNN-1D2026.05 | 0.375 | — | — | |
| PLS vs CNN-1D2026.05 | 0.533 | — | — | |
| TabPFN-Raw vs CNN-1D2026.05 | 0.65 | — | — | |
| Ridge vs CatBoost2026.05 | 0.707 | — | — | |
| CatBoost vs CNN-1D2026.05 | 0.777 | — | — | |
| Ridge vs TabPFN-Raw2026.05 | 0.922 | — | — | |
| Ridge vs PLS2026.05 | 0.967 | — | — | |
| CatBoost vs PLS2026.05 | 0.996 | — | — | |
| CatBoost vs TabPFN-Raw2026.05 | 0.999 | — | — | |
| TabPFN-Raw vs PLS2026.05 | 0.999 | — | — | |
| TabPFN-opt vs CNN-1D2026.05 | 4.9 | — | — | |
| CatBoostCNN-1D available=true2026.05 | — | 3.65 | — | |
| CNN-1DCNN-1D available=true2026.05 | — | 4.55 | — | |
| PLSCNN-1D available=true2026.05 | — | 4.05 | — | |
| RidgeCNN-1D available=true2026.05 | — | 3.075 | — | |
| TabPFN-optCNN-1D available=true2026.05 | — | 1.8 | — | |
| TabPFN-RawCNN-1D available=true2026.05 | — | 3.875 | — |