Σύγκριση μεθόδων

	Σύνολο Bagging	Σύνολο Ενίσχυσης
Πεδίο	Μάθηση Συνόλων Μοντέλων (Ensemble)	Μάθηση Συνόλων Μοντέλων (Ensemble)
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1996	1990
Δημιουργός	Leo Breiman	Robert Schapire
Τύπος	parallel ensemble	sequential ensemble
Θεμελιώδης πηγή	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
Εναλλακτικές ονομασίες	bootstrap aggregating	adaptive boosting, sequential ensemble
Συναφείς	4	4
Σύνοψη	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.	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.