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ResearchFormerly WizWand

Classification on blood-transfusion (Accuracy)

79.9AccuracyLogistic Regression
66.970.27573.6577.025Mar 18, 2025
Updated 5mo ago

Evaluation results

MethodLinks
Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.0379.9
Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.0379.9
TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.0379.1
TabPFNPredictor=TabPFN, Feature Engineering=Base2025.0379
MLPPredictor=MLP, Feature Engineering=LLM-FE2025.0378.2
FeatLLMBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0377.1
OCTreeBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0375.5
CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.0375.1
XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.0375.1
LLM-FEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0375.1
CAAFEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0374.9
OpenFEBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0374.7
CatBoostPredictor=CatBoost, Feature Engineering=Base2025.0374.2
XGBoostPredictor=XGBoost, Feature Engineering=Base2025.0374.2
BaseBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0374.2
AutoFeatBackbone=XGBoost, n (number of samples)=748, p (number of features)=42025.0373.8
MLPPredictor=MLP, Feature Engineering=Base2025.0367.4