Methoden vergleichen

	Bagging-Ensemble	Gradient Boosting
Fachgebiet	Ensemble-Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1996	2001
Urheber	Leo Breiman	Friedman, J. H.
Typ	parallel ensemble	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	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Verwandt	4	5
Zusammenfassung	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.	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.