Classification on balance-scale (Accuracy)
99AccuracyLLM-FE
Evaluation Results
| Method | Links | |
|---|---|---|
| LLM-FEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 99 | |
| OpenFEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 98.6 | |
| BinBase Model=MLP2026.03 | 96.8 | |
| DirBase Model=MLP2026.03 | 96.8 | |
| IRBase Model=MLP2026.03 | 96.8 | |
| CAAFEBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 96.6 | |
| BrenierIRBase Model=MLP, k=152026.03 | 96.5 | |
| IRPBase Model=MLP2026.03 | 96 | |
| BrenierIRBase Model=MLP, k=302026.03 | 96 | |
| BrenierIRBase Model=MLP, k=502026.03 | 95.9 | |
| UncalibratedBase Model=MLP2026.03 | 92.8 | |
| MSBase Model=MLP2026.03 | 92.8 | |
| OIBase Model=MLP2026.03 | 92.8 | |
| TSBase Model=MLP2026.03 | 92.8 | |
| BrenierIRBase Model=linear SVM, k=152026.03 | 92.8 | |
| BrenierIRBase Model=linear SVM, k=302026.03 | 92.8 | |
| BrenierIRBase Model=linear SVM, k=502026.03 | 92.8 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 92.5 | |
| OCTreeBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 88.2 | |
| BinBase Model=linear SVM2026.03 | 88 | |
| UncalibratedBase Model=linear SVM2026.03 | 87.2 | |
| IRBase Model=linear SVM2026.03 | 87.2 | |
| BaseBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 85.6 | |
| FeatLLMBackbone=XGBoost, n (number of samples)=625, p (number of features)=42025.03 | 80 | |
| DirBase Model=linear SVM2026.03 | 73.6 | |
| MSBase Model=linear SVM2026.03 | 73.6 | |
| OIBase Model=linear SVM2026.03 | 73.6 | |
| TSBase Model=linear SVM2026.03 | 73.6 | |
| IRPBase Model=linear SVM2026.03 | 45.6 |