השוואת שיטות

	שק (Bootstrap Aggregating)	ערימה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1996	1992
הוגה השיטה	Breiman, L.	Wolpert, D.H.
סוג	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (heterogeneous meta-learning)
מקור מכונן	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
כינויים	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
קשורות	5	5
תקציר	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.