Vertaile menetelmiä

	Ensemble 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 Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liittyvät	6	5
Tiivistelmä	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.