Wine
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Tabular Anomaly Detection | Wine | AUC-ROC1 | 72 | |
| Local explanation fidelity | Wine | RMSE0.003 | 54 | |
| Regression | wine | MSE0.518 | 49 | |
| Classification | wine | F1 Macro98.44 | 48 | |
| Clustering | Wine | ARI0.93 | 48 | |
| Classification | wine | Accuracy99.1 | 45 | |
| Conditional Shapley value estimation | Wine M=11 | MSEv0.071 | 44 | |
| Anomaly Detection | Wine | AUC-PR100 | 37 | |
| Classification | Wine (test) | Accuracy100 | 35 | |
| Online Class-Incremental Learning | Wine | Final Mean Accuracy98.7 | 26 | |
| Classification | wine | F1 Score98.8 | 26 | |
| Hierarchical Agglomerative Clustering | wine | Dendrogram Purity0.95 | 26 | |
| Regression | Wine | RMSE0.62 | 21 | |
| Classification | Wine (5-fold cross-validation) | Accuracy98.3 | 19 | |
| Counterfactual Explanation Generation | wine | Validity1 | 17 | |
| Multi-class Classification | Wine | Accuracy97.59 | 16 | |
| Clustering | Wine (UCI) | ACC98.31 | 15 | |
| Tabular Classification with Feature Selection | Wine | Accuracy90.28 | 14 | |
| Regression | Wine UCI (test) | RMSE0.61 | 14 | |
| Data Imputation | Wine | MAE0.0768 | 14 | |
| Dynamic Feature Selection | Wine (5-fold CV) | AUAC-F196 | 13 | |
| Classification | Wine | Accuracy98.89 | 13 | |
| Time Series Classification | Wine (test) | Accuracy92.6 | 13 | |
| Node Classification | Wine (test) | Accuracy97.3 | 13 | |
| Classification | Wine 4% | PR AUC60.6 | 12 |
Showing 25 of 120 rows