Porównaj metody

	Extra Trees	Bagging (agregacja bootstrapowa)	Gradient Boosting
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2006	1996	2001
Twórca	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.	Friedman, J. H.
Typ	Ensemble (extremely randomized decision trees)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of decision trees)
Źródło pierwotne	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Inne nazwy	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Pokrewne	5	5	5
Podsumowanie	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.	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.