Methoden vergleichen

	Boosting	Regularisiertes Gradient Boosting
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1990–1997	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Urheber	Schapire, R. E.; Freund, Y.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Typ	Sequential ensemble (iterative reweighting)	Regularized ensemble (additive tree model)
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.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Aliasnamen	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Verwandt	6	6
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 gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.