Vertaile menetelmiä

	Gradient Boosting	Päätöspuu
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2001	1984
Kehittäjä	Friedman, J. H.	Breiman, Friedman, Olshen & Stone
Tyyppi	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)
Alkuperäislähde	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Rinnakkaisnimet	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liittyvät	5	5
Tiivistelmä	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.