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	Bagging (agregacja bootstrapowa)	Drzewo decyzyjne	Extra Trees
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1996	1984	2006
Twórca	Breiman, L.	Breiman, Friedman, Olshen & Stone	Geurts, P.; Ernst, D.; Wehenkel, L.
Typ	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Recursive partitioning (if-then rules)	Ensemble (extremely randomized decision trees)
Źródło pierwotne	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Inne nazwy	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Pokrewne	5	5	5
Podsumowanie	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.