Classification on blood-transfusion (Accuracy)
79.9AccuracyLogistic Regression
Evaluation Results
| Method | Links | |
|---|---|---|
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.03 | 79.9 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.03 | 79.9 | |
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 79.1 | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 79 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 78.2 | |
| FeatLLMBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 77.1 | |
| OCTreeBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 75.5 | |
| CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.03 | 75.1 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 75.1 | |
| LLM-FEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 75.1 | |
| CAAFEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 74.9 | |
| OpenFEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 74.7 | |
| CatBoostPredictor=CatBoost, Feature Engineering=Base2025.03 | 74.2 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 74.2 | |
| BaseBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 74.2 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.03 | 73.8 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 67.4 |