California
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Regression | california | R2 Score84.32 | 49 | |
| Regression | california | RMSE0.3964 | 40 | |
| Regression | California dataset | RMSE0.2 | 30 | |
| Model Compression | california | Accuracy/R283 | 26 | |
| Binary Classification | California 1% | PR AUC32.2 | 24 | |
| Hyperparameter Optimization | California XGB (test) | Final Simple Regret0.0225 | 22 | |
| Hyperparameter Optimization | California LGBM (test) | Final Simple Regret0.0171 | 22 | |
| Membership Inference Attack | California | AUC0.992 | 16 | |
| Visual Place Recognition | California (test) | Recall@155.9 | 16 | |
| Tabular Data Utility | California (test) | AUC0.999 | 14 | |
| Dynamic Feature Selection | California 5-fold CV | AUAC-F183.98 | 13 | |
| Regression | california | MAE0.1397 | 12 | |
| Regression | california | R20.8312 | 12 | |
| Regression | california | RMSE0.1974 | 12 | |
| Imbalanced Classification | California 1% | PR AUC40.1 | 12 | |
| Classification | California 10% | PR AUC80.3 | 12 | |
| Classification | California 20% | PR AUC88.9 | 12 | |
| Privacy risk evaluation | California dataset | DCR74 | 11 | |
| RF compression | california | Performance Score74.6 | 9 | |
| Classification | California | Error Rate12.915 | 9 | |
| Regression | California | Best Training Loss0.3716 | 8 | |
| Regression | A06 california (test) | R2 Score0.967 | 6 | |
| Extreme Precipitation Detection | California n200=537, n300=15 (test) | SEDI (50)98.9 | 5 | |
| Relational Database Generation | California | Cardinality Score99.96 | 5 | |
| Generative Tabular Data Modeling | California Synth | Mean Wasserstein Distance0.086 | 5 |
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