Classification on car
99.9AccuracyCatBoost
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| CatBoostPredictor=CatBoost, Feature Engineering=Base2025.03 | 99.9 | — | — | |
| CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.03 | 99.9 | — | — | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 99.9 | — | — | |
| CAAFEBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.9 | — | — | |
| LLM-FEBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.9 | — | — | |
| AutoFeatBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.8 | — | — | |
| OpenFEBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.8 | — | — | |
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 99.6 | — | — | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 99.5 | — | — | |
| BaseBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.5 | — | — | |
| OCTreeBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 99.5 | — | — | |
| UncalibratedBase Model=MLP2026.03 | 99.1 | — | — | |
| MSBase Model=MLP2026.03 | 99.1 | — | — | |
| OIBase Model=MLP2026.03 | 99.1 | — | — | |
| TSBase Model=MLP2026.03 | 99.1 | — | — | |
| BinBase Model=MLP2026.03 | 98.8 | — | — | |
| DirBase Model=MLP2026.03 | 98.8 | — | — | |
| BrenierIRBase Model=MLP, k=302026.03 | 98.8 | — | — | |
| BrenierIRBase Model=MLP, k=502026.03 | 98.8 | — | — | |
| IRPBase Model=MLP2026.03 | 98.6 | — | — | |
| IRBase Model=MLP2026.03 | 98.6 | — | — | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 98.4 | — | — | |
| BrenierIRBase Model=MLP, k=152026.03 | 96.5 | — | — | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 95 | — | — | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 92.9 | — | — | |
| IRBase Model=linear SVM2026.03 | 90.8 | — | — | |
| BinBase Model=linear SVM2026.03 | 89.3 | — | — | |
| UncalibratedBase Model=linear SVM2026.03 | 88.2 | — | — | |
| IRPBase Model=linear SVM2026.03 | 88.2 | — | — | |
| BrenierIRBase Model=linear SVM, k=302026.03 | 88.1 | — | — | |
| BrenierIRBase Model=linear SVM, k=502026.03 | 87.9 | — | — | |
| BrenierIRBase Model=linear SVM, k=152026.03 | 86 | — | — | |
| SVM2026.04 | 81.7 | 0.338 | — | |
| FeatLLMBackbone=XGBoost, n (number of samples)=1728, p (number of features)=62025.03 | 80.8 | — | — | |
| DFSOS-12026.04 | 77 | 1.018 | 79.3 | |
| DFSOS-22026.04 | 77 | 0.937 | 79.3 | |
| ADMM2026.04 | 76.5 | 0.48 | 61.2 | |
| MSBase Model=linear SVM2026.03 | 75.4 | — | — | |
| APG2026.04 | 75 | 0.971 | 97.7 | |
| DirBase Model=linear SVM2026.03 | 73.7 | — | — | |
| 1-NN2026.04 | 71.7 | 0.025 | — | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.03 | 69.6 | — | — | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.03 | 69 | — | — | |
| 10-NN2026.04 | 60 | 0.016 | — | |
| 5-NN2026.04 | 58.3 | 0.023 | — | |
| OIBase Model=linear SVM2026.03 | 26 | — | — | |
| TSBase Model=linear SVM2026.03 | 26 | — | — |