Сравнение методов

	Бустинг	Регуляризованный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1990–1997	2001–2016
Автор метода	Schapire, R. E.; Freund, Y.	Friedman, J. H.; extended by Chen & Guestrin
Тип	Sequential ensemble (iterative reweighting)	Regularized ensemble (boosting with shrinkage/penalty)
Основополагающий источник	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Другие названия	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Связанные	6	5
Сводка	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.