Methoden vergleichen

	Regularized Boosting	Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2001–2016	2001
Urheber	Friedman, J. H.; extended by Chen & Guestrin	Friedman, J. H.
Typ	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (sequential boosting of decision trees)
Wegweisende Quelle	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Aliasnamen	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Verwandt	5	5
Zusammenfassung	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.	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.