Methoden vergleichen

	Bagging (Bootstrap Aggregating)	Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1996	2001
Urheber	Breiman, L.	Friedman, J. H.
Typ	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of decision trees)
Wegweisende Quelle	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.
Aliasnamen	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Verwandt	5	5
Zusammenfassung	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.