Methoden vergleichen

	Boosting	Regularized Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1990–1997	2001–2016
Urheber	Schapire, R. E.; Freund, Y.	Friedman, J. H.; extended by Chen & Guestrin
Typ	Sequential ensemble (iterative reweighting)	Regularized ensemble (boosting with shrinkage/penalty)
Wegweisende Quelle	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Aliasnamen	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Verwandt	6	5
Zusammenfassung	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.