Сравнение методов

	Голосующая ансамблевая модель	Бустинг	Чрезвычайно случайные деревья
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1990s–2004	1990–1997	2006
Автор метода	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Schapire, R. E.; Freund, Y.	Geurts, P.; Ernst, D.; Wehenkel, L.
Тип	Ensemble (combination of multiple classifiers by vote)	Sequential ensemble (iterative reweighting)	Ensemble (extremely randomized decision trees)
Основополагающий источник	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Другие названия	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Связанные	5	6	5
Сводка	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.