Сравнение методов

	AdaBoost	Градиентный бустинг
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1997	2001
Автор метода	Freund, Y. & Schapire, R.E.	Friedman, J. H.
Тип	Ensemble (sequential boosting of weak learners)	Ensemble (sequential boosting of decision trees)
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 (Adaptive Boosting), adaptive boosting, adaptif artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Связанные	5	5
Сводка	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.