مقایسهٔ روشها
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| گرادیان بوستینگ (Gradient Boosting)× | لایتجیبیام (LightGBM)× | تقویت گرادیان منظمشده× | |
|---|---|---|---|
| حوزه | یادگیری ماشین | یادگیری ماشین | یادگیری ماشین |
| خانواده | Machine learning | Machine learning | Machine learning |
| سال پیدایش≠ | 2001 | 2017 | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) |
| پدیدآور≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | Chen, T. & Guestrin, C. (building on Friedman, J. H.) |
| نوع≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Regularized ensemble (additive tree model) |
| منبع بنیادین≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | 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 (NeurIPS) 30, 3146–3154. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ |
| نامهای دیگر | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting |
| مرتبط≠ | 5 | 5 | 6 |
| خلاصه≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data. |
| ScholarGateمجموعهداده ↗ |
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