Classification on Adult-Gender real-world (test)
87.2AccuracyRMB-CLE-via-LGBM
Evaluation Results
| Method | Links | |
|---|---|---|
| RMB-CLE-via-LGBMStrategy=Robust Multi-Task Boosting, Backbone=LightGBM2026.02 | 87.2 | |
| ST-LGBMStrategy=Single-Task, Backbone=LightGBM2026.02 | 85.5 | |
| DP-LGBMStrategy=Data-Parallel, Backbone=LightGBM2026.02 | 85.4 | |
| TaF-LGBMStrategy=Task-at-a-Time, Backbone=LightGBM2026.02 | 85.4 | |
| R-MTGBStrategy=Robust Multi-Task, Backbone=Gradient Boosting2026.02 | 84.9 | |
| RMB-CLE-via-MTGBStrategy=Robust Multi-Task Boosting, Backbone=Multi-Task Gradient Boosting2026.02 | 84.8 | |
| MTGBStrategy=Multi-Task, Backbone=Gradient Boosting2026.02 | 84.8 | |
| ST-GBStrategy=Single-Task, Backbone=Gradient Boosting2026.02 | 84.1 | |
| DP-GBStrategy=Data-Parallel, Backbone=Gradient Boosting2026.02 | 83.7 | |
| TaF-GBStrategy=Task-at-a-Time, Backbone=Gradient Boosting2026.02 | 83.7 |