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	Ensemble głosujący	Bagging (agregacja bootstrapowa)	Extra Trees
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1990s–2004	1996	2006
Twórca	Lam & Suen; Kuncheva, L. I. (systematic treatment)	Breiman, L.	Geurts, P.; Ernst, D.; Wehenkel, L.
Typ	Ensemble (combination of multiple classifiers by vote)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (extremely randomized decision trees)
Źródło pierwotne	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Inne nazwy	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Pokrewne	5	5	5
Podsumowanie	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.