Comparer des méthodes

	Bagging (Bootstrap Aggregating)	Boosting en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	2001
Auteur d'origine	Breiman, L.	Oza, N. C. & Russell, S.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Online ensemble (incremental boosting)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Oza, N. C., & Russell, S. (2001). Online Bagging and Boosting. In Artificial Intelligence and Statistics 2001 (pp. 105–112). Morgan Kaufmann.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	streaming boosting, incremental boosting, online AdaBoost, online ensemble boosting
Apparentées	5	6
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.	Online Boosting adapts the classical boosting framework to data streams, updating an ensemble of weak learners one example at a time without storing the full dataset. The Oza-Russell formulation approximates AdaBoost's reweighting using Poisson-sampled instance counts, enabling accurate, adaptive classification in real-time or resource-constrained environments.