Methoden vergleichen

	Boosting	XGBoost
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1990–1997	2016
Urheber	Schapire, R. E.; Freund, Y.	Chen, T. & Guestrin, C.
Typ	Sequential ensemble (iterative reweighting)	Ensemble (gradient-boosted decision trees)
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, 785–794.
Aliasnamen	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	XGBoost, extreme gradient boosting, scalable tree 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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.