Vertaile menetelmiä

	Boosting	XGBoost
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1990–1997	2016
Kehittäjä	Schapire, R. E.; Freund, Y.	Chen, T. & Guestrin, C.
Tyyppi	Sequential ensemble (iterative reweighting)	Ensemble (gradient-boosted decision trees)
Alkuperäislähde	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.
Rinnakkaisnimet	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Liittyvät	6	5
Tiivistelmä	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.