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ResearchFormerly WizWand

Classification on balance-scale (Accuracy)

99AccuracyLLM-FE
43.46457.88272.386.718Mar 18, 2025May 16, 2025Jul 15, 2025Sep 13, 2025Nov 11, 2025Jan 10, 2026Mar 11, 2026
Updated 5mo ago

Evaluation results

MethodLinks
LLM-FEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0399
OpenFEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0398.6
BinBase Model=MLP2026.0396.8
DirBase Model=MLP2026.0396.8
IRBase Model=MLP2026.0396.8
CAAFEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0396.6
BrenierIRBase Model=MLP, k=152026.0396.5
IRPBase Model=MLP2026.0396
BrenierIRBase Model=MLP, k=302026.0396
BrenierIRBase Model=MLP, k=502026.0395.9
UncalibratedBase Model=MLP2026.0392.8
MSBase Model=MLP2026.0392.8
OIBase Model=MLP2026.0392.8
TSBase Model=MLP2026.0392.8
BrenierIRBase Model=linear SVM, k=152026.0392.8
BrenierIRBase Model=linear SVM, k=302026.0392.8
BrenierIRBase Model=linear SVM, k=502026.0392.8
AutoFeatBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0392.5
OCTreeBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0388.2
BinBase Model=linear SVM2026.0388
UncalibratedBase Model=linear SVM2026.0387.2
IRBase Model=linear SVM2026.0387.2
BaseBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0385.6
FeatLLMBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.0380
DirBase Model=linear SVM2026.0373.6
MSBase Model=linear SVM2026.0373.6
OIBase Model=linear SVM2026.0373.6
TSBase Model=linear SVM2026.0373.6
IRPBase Model=linear SVM2026.0345.6