Classification on heart (accuracy)
88.2AccuracyTabPFN
Evaluation results
| Method | Links | |
|---|---|---|
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 88.2 | |
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 88 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 86.6 | |
| LLM-FEBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 86.6 | |
| FeatLLMBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 86.5 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 85.8 | |
| BaseBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 85.8 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 85.7 | |
| CfsSubset Eval-F(#)Classification Algorithm=Linear SVM2026.03 | 85.56 | |
| OpenFEBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 85.4 | |
| OCTreeBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 85.2 | |
| CAAFEBackbone=XGBoost, n (number of samples)=918, p (number of features)=112025.03 | 84.9 | |
| OriginalClassification Algorithm=Linear SVM2026.03 | 84.81 | |
| ε-BAEN-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 84.6 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 84.4 | |
| BQ-SVMKernel=linear, Noise Level=0%2026.04 | 84.3 | |
| BALS-SVMKernel=linear, Noise Level=0%2026.04 | 84.3 | |
| BALS-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 84.3 | |
| BAEN-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 84.3 | |
| WARA-F(#)Classification Algorithm=Linear SVM2026.03 | 84.07 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 84 | |
| Pin-SVMKernel=linear, Noise Level=0%2026.04 | 83.9 | |
| ALS-SVMKernel=linear, Noise Level=0%2026.04 | 83.9 | |
| EN-SVMKernel=linear, Noise Level=0%2026.04 | 83.9 | |
| BAEN-SVMKernel=linear, Noise Level=0%2026.04 | 83.9 | |
| ε-BAEN-SVMKernel=linear, Noise Level=0%2026.04 | 83.9 | |
| ALS-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 83.9 | |
| EN-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 83.9 | |
| FARNem-F(#)Classification Algorithm=Linear SVM2026.03 | 83.7 | |
| RSFSAID-F(#)Classification Algorithm=Linear SVM2026.03 | 83.7 | |
| FSbuHD-F(#)Classification Algorithm=Linear SVM2026.03 | 83.7 | |
| Pin-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 83.6 | |
| BQ-SVMKernel=linear, Noise Level=25% feature noise2026.04 | 83.6 | |
| BAEN-SVMKernel=linear, Noise Level=25% label noise2026.04 | 81.9 | |
| BQ-SVMKernel=linear, Noise Level=25% label noise2026.04 | 81.3 | |
| CfsSubset Eval-F(#)Classification Algorithm=KNN2026.03 | 81.11 | |
| FSbuHD-F(#)Classification Algorithm=KNN2026.03 | 81.11 | |
| EN-SVMKernel=linear, Noise Level=25% label noise2026.04 | 80.9 | |
| ε-BAEN-SVMKernel=linear, Noise Level=25% label noise2026.04 | 80.9 | |
| WARA-F(#)Classification Algorithm=KNN2026.03 | 80.74 | |
| RSFSAID-F(#)Classification Algorithm=KNN2026.03 | 80.37 | |
| Pin-SVMKernel=linear, Noise Level=25% label noise2026.04 | 80.3 | |
| ε-BAEN SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 79.9 | |
| CfsSubset Eval-F(#)Classification Algorithm=Complex Tree2026.03 | 79.63 | |
| FSbuHD-F(#)Classification Algorithm=Complex Tree2026.03 | 79.63 | |
| ALS-SVMKernel=linear, Noise Level=25% label noise2026.04 | 79.6 | |
| BALS-SVMKernel=linear, Noise Level=25% label noise2026.04 | 79.6 | |
| BAEN-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 79.3 | |
| EN-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 78.9 | |
| BQ-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 78.9 | |
| BALS-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 78.9 | |
| OriginalClassification Algorithm=KNN2026.03 | 78.52 | |
| Pin-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 78.2 | |
| RSFSAID-F(#)Classification Algorithm=Complex Tree2026.03 | 78.15 | |
| ALS-SVMKernel=RBF, Noise level=25%, Noise type=label noise2026.04 | 77.6 | |
| WARA-F(#)Classification Algorithm=Complex Tree2026.03 | 77.41 | |
| FARNem-F(#)Classification Algorithm=KNN2026.03 | 76.67 | |
| OriginalClassification Algorithm=Complex Tree2026.03 | 75.56 | |
| FARNem-F(#)Classification Algorithm=Complex Tree2026.03 | 75.56 |