Wine quality
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Imbalanced Classification | wine quality | F1-Score62.7 | 25 | |
| Counterfactual Explanation Generation | Wine Quality | R Score0.998 | 23 | |
| Classification | Wine Quality | AW0.984 | 14 | |
| Regression | Wine Quality (test) | MSE0.439 | 11 | |
| Attribute selection in the counterfactual task | Wine Quality | NLL35.6 | 9 | |
| Classification | Wine Quality White UCIrvine | Macro F1 Score75.2 | 9 | |
| Classification | Wine Quality Red UCIrvine | Macro F170.7 | 9 | |
| Bandit Learning | Wine Quality Real Data Benign | Mean Regret666.74 | 8 | |
| Regression | Wine Quality Regime 2: High-Complexity | MSE0.8843 | 6 | |
| Classification | Wine Quality (test) | Test Error24.1 | 6 | |
| Synthetic Data Generation | Wine Quality | Average Runtime (seconds)0.0566 | 6 | |
| Classification | Wine Quality (10-fold CV) | P-Value0.001 | 5 | |
| Tabular Regression | Wine Quality (UCI) | Total experiment time (s)3.4 | 4 | |
| Counterfactual Explanation Generation | wine-quality white | Validity1 | 4 | |
| Counterfactual Explanation Generation | wine-quality-red | Validity100 | 4 | |
| Outlier Explanation | wine-quality white | Average Run Time (s)0.13 | 4 | |
| Outlier Explanation | Wine Quality Red | Avg Run Time (s)0.017 | 4 | |
| Missing Data Recovery | Wine Quality | MSE30.53 | 2 | |
| Regression | Wine Quality | MSE0.345 | 2 | |
| Regression | Wine quality white (test) | MSE0.8659 | 2 | |
| Regression | Wine quality red (test) | MSE0.7223 | 2 | |
| Cell-level outlier detection | Wine Quality | Metric- | 0 |
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