Jämför metoder

	Boosting Ensemble	Bagging Ensemble
Ämnesområde	Ensembleinlärning	Ensembleinlärning
Familj	Machine learning	Machine learning
Ursprungsår	1990	1996
Upphovsperson	Robert Schapire	Leo Breiman
Typ	sequential ensemble	parallel ensemble
Ursprungskälla	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.
Alias	adaptive boosting, sequential ensemble	bootstrap aggregating
Närliggande	4	4
Sammanfattning	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.