Comparer des méthodes

	Ensemble par vote	Bagging (Bootstrap Aggregating)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990s–2004	1996
Auteur d'origine	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Breiman, L.
Type	Ensemble (combination of multiple classifiers by vote)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Source fondatrice	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Alias	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Apparentées	5	5
Résumé	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.	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.