Comparer des méthodes

	Ensemble par vote	Extra Trees
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990s–2004	2006
Auteur d'origine	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Geurts, P.; Ernst, D.; Wehenkel, L.
Type	Ensemble (combination of multiple classifiers by vote)	Ensemble (extremely randomized decision trees)
Source fondatrice	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Alias	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.