Methoden vergleichen

	Bagging (Bootstrap Aggregating)	AdaBoost	Random Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	1996	1997	2001
Urheber	Breiman, L.	Freund, Y. & Schapire, R.E.	Breiman, L.
Typ	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of weak learners)	Ensemble (bagging of decision trees)
Wegweisende Quelle	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliasnamen	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwandt	5	5	4
Zusammenfassung	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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.