Сравнение методов

	Градиентный бустинг	Регуляризованный градиентный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Автор метода	Friedman, J. H.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Тип	Ensemble (sequential boosting of decision trees)	Regularized ensemble (additive tree model)
Основополагающий источник	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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.
Другие названия	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Связанные	5	6
Сводка	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.	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.