Porovnat metody

	Bagging (Bootstrap Aggregating)	Stacking
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1996	1992
Tvůrce	Breiman, L.	Wolpert, D.H.
Typ	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (heterogeneous meta-learning)
Původní zdroj	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Další názvy	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Příbuzné	5	5
Shrnutí	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.	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.