Vertaile menetelmiä

	AdaBoost	XGBoost
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1997	2016
Kehittäjä	Freund, Y. & Schapire, R.E.	Chen, T. & Guestrin, C.
Tyyppi	Ensemble (sequential boosting of weak learners)	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 (Adaptive Boosting), adaptive boosting, adaptif artırma	XGBoost, extreme gradient boosting, scalable tree boosting
Liittyvät	5	5
Tiivistelmä	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.