Sammenlign metoder

	Bagging (Bootstrap Aggregating)	Boosting	Stacking
Fagfelt	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Opprinnelsesår	1996	1990–1997	1992
Opphavsperson	Breiman, L.	Schapire, R. E.; Freund, Y.	Wolpert, D.H.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Ensemble (heterogeneous meta-learning)
Opprinnelig kilde	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.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Relaterte	5	6	5
Sammendrag	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.