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| Robust LightGBM× | CatBoost× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2017 (LightGBM); robust variants widely adopted 2018–present | 2018 |
| Người khởi xướng≠ | Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H. | Prokhorenkova, L. et al. (Yandex) |
| Loại≠ | Ensemble (gradient boosted decision trees with robust loss) | Gradient boosting on decision trees |
| Công trình gốc≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ |
| Tên gọi khác | Robust LGBM, LightGBM with Huber loss, outlier-resistant gradient boosting, robust gradient boosted trees | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable. | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. |
| ScholarGateBộ dữ liệu ↗ |
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