השוואת שיטות

	שק (Bootstrap Aggregating)	בוסטינג	שקית חסינה (Robust Bagging)
תחום	למידת מכונה	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning	Machine learning
שנת המקור	1996	1990–1997	1996–2000s
הוגה השיטה	Breiman, L.	Schapire, R. E.; Freund, Y.	Breiman, L. (bagging); robust variants developed by various authors in 2000s
סוג	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Sequential ensemble (iterative reweighting)	Ensemble (robust bootstrap aggregating)
מקור מכונן	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. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
כינויים	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing
קשורות	5	6	6
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.	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.	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.