Classification on eucalyptus (Accuracy)
71.5AccuracyTabPFN
Evaluation Results
| Method | Links | |
|---|---|---|
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 71.5 | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 71.2 | |
| CAAFEBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 67.9 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.03 | 67.1 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 66.8 | |
| LLM-FEBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 66.8 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.03 | 66.7 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 66.4 | |
| OpenFEBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 66.3 | |
| OCTreeBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 65.8 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 65.5 | |
| BaseBackbone=XGBoost, n (number of samples)=736, p (number of features)=192025.03 | 65.5 | |
| CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.03 | 50.9 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 45.6 | |
| CatBoostPredictor=CatBoost, Feature Engineering=Base2025.03 | 43.6 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 41.4 |