Classification on pc1 (Accuracy)
93.7Accuracy (pc1)TabPFN
Evaluation Results
| Method | Links | |
|---|---|---|
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 93.7 | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 93.6 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 93.5 | |
| LLM-FEBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.5 | |
| OCTreeBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.4 | |
| FeatLLMBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.3 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 93.1 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 93.1 | |
| BaseBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.1 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.1 | |
| OpenFEBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 93.1 | |
| CAAFEBackbone=XGBoost, n (number of samples)=1109, p (number of features)=212025.03 | 92.9 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 90.4 |