Compară metode

	Bagging (Agregare Bootstrap)	Gradient Boosting
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996	2001
Autorul original	Breiman, L.	Friedman, J. H.
Tip	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of decision trees)
Sursa seminală	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Denumiri alternative	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Înrudite	5	5
Rezumat	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.