Comparer des méthodes

	Boosting	Bagging (Bootstrap Aggregating)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	1996
Auteur d'origine	Schapire, R. E.; Freund, Y.	Breiman, L.
Type	Sequential ensemble (iterative reweighting)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Source fondatrice	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Apparentées	6	5
Résumé	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.	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.