השוואת שיטות

	שיטת אנסמבל חיזוק (Boosting Ensemble)	Bagging Ensemble
תחום	למידת אנסמבל	למידת אנסמבל
משפחה	Machine learning	Machine learning
שנת המקור	1990	1996
הוגה השיטה	Robert Schapire	Leo Breiman
סוג	sequential ensemble	parallel ensemble
מקור מכונן	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
כינויים	adaptive boosting, sequential ensemble	bootstrap aggregating
קשורות	4	4
תקציר	Boosting is an ensemble method that sequentially trains weak learners and combines them into a strong predictor by focusing on samples that previous models misclassified. Each new weak learner is weighted according to the difficulty of its training task, and final predictions are made via weighted voting. Pioneered by Schapire (1990) and refined in AdaBoost (Freund & Schapire, 1997), boosting converts weak learners (barely better than random) into strong learners through sequential reweighting.	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.