Comparer des méthodes

	Bagging (Bootstrap Aggregating)	AdaBoost	Gradient Boosting
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1996	1997	2001
Auteur d'origine	Breiman, L.	Freund, Y. & Schapire, R.E.	Friedman, J. H.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of weak learners)	Ensemble (sequential boosting of decision trees)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Apparentées	5	5	5
Résumé	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.