Comparer des méthodes

	Empilement bayésien (Bayesian stacking)	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	1990s–2004
Auteur d'origine	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Bayesian ensemble combination	Ensemble (combination of multiple classifiers by vote)
Source fondatrice	Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	6	5
Résumé	Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation.	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.