Tabular Classification on IEEECIS
83.6Clean AccuracyRandom Forest
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Random ForestRobustness technique=None2023.06 | 83.6 | 48 | |
| Light Gradient Boosting (LGBM)Robustness technique=None2023.06 | 81.2 | 53.9 | |
| Random ForestRobustness technique=Robust Embeddings (-R)2023.06 | 81 | 81 | |
| Light Gradient Boosting (LGBM)Robustness technique=Robust Embeddings (-R)2023.06 | 79.3 | 78.5 | |
| CatBoost (CB)Robustness technique=None2023.06 | 76.5 | 51.1 | |
| CatBoost (CB)Robustness technique=Robust Embeddings (-R)2023.06 | 76.1 | 72 | |
| Random ForestRobustness technique=Chen et al. (2019) (-C)2023.06 | 69 | 69 | |
| Gradient Boosted Stumps (GBS)Robustness technique=None2023.06 | 66.9 | 44.7 | |
| Gradient Boosted Stumps (GBS)Robustness technique=Robust Embeddings (-R)2023.06 | 66.3 | 66.3 | |
| Gradient Boosted Stumps (GBS)Robustness technique=Wang et al. (2020) (-W)2023.06 | 52.4 | 11.1 |