Classification on Creditg
79.4AccuracyTabPFN
Evaluation results
| Method | Links | |
|---|---|---|
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 79.4 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.03 | 78 | |
| ReSSData augmentation=true, Reasoning path structure=x → z → y2026.04 | 77.67 | |
| XGBoost2026.04 | 77 | |
| TabPFN2026.04 | 76.67 | |
| ReSSData augmentation=false, Reasoning path structure=x → z → y2026.04 | 76.67 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 76.6 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.03 | 76.4 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 75.1 | |
| TabNet2026.04 | 75 | |
| DRC + SFTReasoning path structure=x → z → y2026.04 | 74 | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 72.8 | |
| Decision Tree2026.04 | 72.4 | |
| CatBoostPredictor=CatBoost, Feature Engineering=Base2025.03 | 71.4 | |
| Direct SFTReasoning path structure=x → y2026.04 | 71.2 | |
| Direct RLReasoning path structure=x → z → y2026.04 | 70 | |
| CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.03 | 70 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 63.3 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 55.8 |