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	Drzewo decyzyjne typu ensemble	Bagging (agregacja bootstrapowa)	Extra Trees
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1996–2000	1996	2006
Twórca	Breiman, L.; Dietterich, T. G.	Breiman, L.	Geurts, P.; Ernst, D.; Wehenkel, L.
Typ	Ensemble (multiple decision trees combined)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (extremely randomized decision trees)
Źródło pierwotne	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.	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	decision tree ensemble, ensemble of decision trees, combined decision trees, multiple classifier system (decision trees)	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Pokrewne	6	5	5
Podsumowanie	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.	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.