Classification on Vehicle (Accuracy)
85.6AccuracyTabPFN
Evaluation Results
| Method | Links | |
|---|---|---|
| TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.03 | 85.6 | |
| TabPFNPredictor=TabPFN, Feature Engineering=Base2025.03 | 85.2 | |
| BrenierIRBase Model=MLP, k=152026.03 | 84.4 | |
| IRBase Model=MLP2026.03 | 83.5 | |
| UncalibratedBase Model=MLP2026.03 | 82.9 | |
| DirBase Model=MLP2026.03 | 82.9 | |
| OIBase Model=MLP2026.03 | 82.9 | |
| TSBase Model=MLP2026.03 | 82.9 | |
| BrenierIRBase Model=MLP, k=502026.03 | 82.9 | |
| BinBase Model=MLP2026.03 | 82.4 | |
| MSBase Model=MLP2026.03 | 82.4 | |
| NS-TDFLOPs=2560, Param=13082025.06 | 81.76 | |
| NS-TD2025.06 | 81.76 | |
| IRPBase Model=MLP2026.03 | 81.2 | |
| MLPFLOPs=896, Param=4762025.06 | 81.06 | |
| MLP2025.06 | 81.06 | |
| BrenierIRBase Model=MLP, k=302026.03 | 80.9 | |
| AutoFeatBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 78.8 | |
| OpenFEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 78.5 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.03 | 77.2 | |
| CAAFEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 77.1 | |
| Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.03 | 76.9 | |
| XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.03 | 76.9 | |
| LLM-FEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 76.9 | |
| NS-SRFLOPs=1728, Param=8922025.06 | 76.76 | |
| NS-SR2025.06 | 76.76 | |
| UncalibratedBase Model=linear SVM2026.03 | 76.5 | |
| IRBase Model=linear SVM2026.03 | 76.5 | |
| BinBase Model=linear SVM2026.03 | 75.9 | |
| KANFLOPs=12138, Param=40322025.06 | 75.76 | |
| KAN2025.06 | 75.76 | |
| XGBoostPredictor=XGBoost, Feature Engineering=Base2025.03 | 75.4 | |
| BaseBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 75.4 | |
| OCTreeBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 75.3 | |
| BrenierIRBase Model=linear SVM, k=152026.03 | 74.9 | |
| BrenierIRBase Model=linear SVM, k=502026.03 | 74.7 | |
| FeatLLMBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.03 | 74.4 | |
| BrenierIRBase Model=linear SVM, k=302026.03 | 73.3 | |
| CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.03 | 72.5 | |
| CatBoostPredictor=CatBoost, Feature Engineering=Base2025.03 | 71.9 | |
| MLPPredictor=MLP, Feature Engineering=LLM-FE2025.03 | 67.3 | |
| FAMeXClassifier=Random Forest, Feature Importance Subset=Top 30%2026.05 | 64.74 | |
| FAMeXFeature selection=Top 30%, Classifier=Decision Tree2026.05 | 61.38 | |
| FAMeXClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.05 | 60.29 | |
| MSBase Model=linear SVM2026.03 | 58.8 | |
| MLPPredictor=MLP, Feature Engineering=Base2025.03 | 58.3 | |
| DirBase Model=linear SVM2026.03 | 57.6 | |
| FAMeXFeature selection=Bottom 30%, Classifier=Decision Tree2026.05 | 57.33 | |
| PFIFeature selection=Bottom 30%, Classifier=Decision Tree2026.05 | 53.91 | |
| SHAPFeature selection=Bottom 30%, Classifier=Decision Tree2026.05 | 53.67 | |
| SHAPClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.05 | 53.5 | |
| PFIClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.05 | 53.5 | |
| FAMeXClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.05 | 45.11 | |
| SHAPClassifier=Random Forest, Feature Importance Subset=Top 30%2026.05 | 44.52 | |
| PFIClassifier=Random Forest, Feature Importance Subset=Top 30%2026.05 | 44.1 | |
| FAMeXClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.05 | 43.53 | |
| PFIFeature selection=Top 30%, Classifier=Decision Tree2026.05 | 43.41 | |
| SHAPFeature selection=Top 30%, Classifier=Decision Tree2026.05 | 43.29 | |
| OIBase Model=linear SVM2026.03 | 41.2 | |
| TSBase Model=linear SVM2026.03 | 41.2 | |
| SHAPClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.05 | 38.27 | |
| PFIClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.05 | 38.25 | |
| SHAPClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.05 | 31.13 | |
| PFIClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.05 | 31.09 | |
| IRPBase Model=linear SVM2026.03 | 25.9 |