Wine
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Data Imputation | wine (test) | RMSE0.0997 | 205 | |
| Communicative Success | WINE (test) | Success Rate100 | 88 | |
| Tabular Anomaly Detection | Wine | AUC-ROC1 | 72 | |
| Imputation | Wine MNAR | Wasserstein Distance0.8058 | 63 | |
| Imputation | Wine MAR | Wasserstein Distance0.6428 | 63 | |
| Imputation | Wine MCAR | Wasserstein Distance0.6069 | 63 | |
| Local explanation fidelity | Wine | RMSE0.003 | 54 | |
| Clustering | Wine | ARI0.93 | 53 | |
| Regression | wine | MSE0.518 | 49 | |
| Classification | wine | F1 Macro98.44 | 48 | |
| Classification | Wine (test) | Accuracy100 | 45 | |
| Classification | wine | Accuracy99.1 | 45 | |
| Conditional Shapley value estimation | Wine M=11 | MSEv0.071 | 44 | |
| Anomaly Detection | Wine | AUC-PR100 | 37 | |
| Classification | wine | F1 Score98.8 | 30 | |
| Online Class-Incremental Learning | Wine | Final Mean Accuracy98.7 | 26 | |
| Hierarchical Agglomerative Clustering | wine | Dendrogram Purity0.95 | 26 | |
| Regression | Wine | NLL0.86 | 22 | |
| Regression | Wine | RMSE0.62 | 21 | |
| Linear Regression | wine (val) | MSE0.6319 | 19 | |
| Classification | Wine (5-fold cross-validation) | Accuracy98.3 | 19 | |
| Counterfactual Explanation Generation | wine | Validity1 | 17 | |
| Mixing matrix estimation | Wine Semi-synthetic | Relative Frobenius Error0.0342 | 16 | |
| Multi-class Classification | Wine | Accuracy97.59 | 16 | |
| Clustering | Wine (UCI) | ACC98.31 | 15 |
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