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	Bagging (Bootstrap Aggregating)	Extra Trees	Random Forest
Ämnesområde	Maskininlärning	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning	Machine learning
Ursprungsår	1996	2006	2001
Upphovsperson	Breiman, L.	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
Typ	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (extremely randomized decision trees)	Ensemble (bagging of decision trees)
Ursprungskälla	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Närliggande	5	5	4
Sammanfattning	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.