Módszerek összehasonlítása

	Bagging (Bootstrap Aggregating)	Halmozás
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1996	1992
Megalkotó	Breiman, L.	Wolpert, D.H.
Típus	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (heterogeneous meta-learning)
Alapmű	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Alternatív nevek	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Kapcsolódó	5	5
Összefoglaló	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.