German Credit
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Global Counterfactual Explanations | German Credit | Effectiveness100 | 36 | |
| Credit Risk Prediction | German Credit (test) | Clean Accuracy69.7 | 31 | |
| Coreset Construction | German Credit | Wasserstein Distance0.23 | 28 | |
| Classification | German Credit (test) | Accuracy77.66 | 28 | |
| Binary decision | German Credit | Accuracy68.9 | 27 | |
| Classification | German Credit UCIrvine | Macro F185.32 | 25 | |
| DP Synthetic Data Generation | German Credit | TSTR78.1 | 24 | |
| Recourse Cost Evaluation | German Credit | Recourse Cost0.02 | 21 | |
| Predictive modeling | German credit | Accuracy (Acc)81.25 | 20 | |
| Fairness Evaluation | German Credit Dataset | Equal Opportunity-0.13 | 17 | |
| Classification | German Credit UCIrvine (5-fold cross-val) | Macro F10.796 | 17 | |
| Classification | German Credit | F1 Score92.5 | 15 | |
| Fair Classification | German Credit (test) | Equal Opportunity Difference5.15 | 15 | |
| Classification | German Credit | AUROC81.5 | 13 | |
| Fairness Mitigation | German Credit | AOD-0.0961 | 12 | |
| Fairness Mitigation Evaluation | German Credit (test) | DPD-0.1068 | 12 | |
| Local Explanation | German Credit | KL Divergence0 | 10 | |
| Classification | German Credit | AU-ARC0.8388 | 10 | |
| Bayesian Logistic Regression | German Credit | NLL0.4765 | 9 | |
| Fair Decision Making | German Credit Age 30 (test) | Total Number of Interventions7.4 | 9 | |
| Fair Decision Making | German Credit Gender 30 (test) | Total Interventions16.2 | 9 | |
| Group Fairness | German Credit Age | Demographic Parity0.005 | 9 | |
| Group Fairness | German Credit Gender | Demographic Parity0.038 | 9 | |
| Tabular Synthetic Data Generation | German Credit | KS Statistic0.022 | 8 | |
| Classification | German Credit | Classification Error33 | 8 |
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