Comparer des méthodes

	Ensemble de Bagging	AdaBoost
Domaine	Apprentissage ensembliste	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1997
Auteur d'origine	Leo Breiman	Freund, Y. & Schapire, R.E.
Type	parallel ensemble	Ensemble (sequential boosting of weak learners)
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.
Alias	bootstrap aggregating	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
Apparentées	4	5
Résumé	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	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.