Vertaile menetelmiä

	XGBoost	Päätöspuu
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2016	1984
Kehittäjä	Chen, T. & Guestrin, C.	Breiman, Friedman, Olshen & Stone
Tyyppi	Ensemble (gradient-boosted decision trees)	Recursive partitioning (if-then rules)
Alkuperäislähde	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Rinnakkaisnimet	XGBoost, extreme gradient boosting, scalable tree boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liittyvät	5	5
Tiivistelmä	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.