Comparer des méthodes

	Bayesian Bagging	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001	1990s–2004
Auteur d'origine	Clyde, M. & Lee, H. (building on Rubin's Bayesian bootstrap, 1981)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble (Bayesian bootstrap aggregation)	Ensemble (combination of multiple classifiers by vote)
Source fondatrice	Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001).	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	Bayesian bootstrap aggregation, BB-ensemble, Bayesian model averaging via bootstrap, Bayesian bagged ensemble	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	6	5
Résumé	Bayesian Bagging replaces the classical bootstrap with the Bayesian bootstrap — drawing Dirichlet-distributed weights over training observations rather than sampling with replacement — and trains an ensemble of base learners under those weights. The result is a principled ensemble that approximates a Bayesian posterior over predictions, yielding calibrated uncertainty estimates alongside strong predictive accuracy.	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.