Comparer des méthodes

	Arbre de décision ensembliste	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996–2000	1990s–2004
Auteur d'origine	Breiman, L.; Dietterich, T. G.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Ensemble (multiple decision trees combined)	Ensemble (combination of multiple classifiers by vote)
Source fondatrice	Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	6	5
Résumé	Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.	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.