Vertaile menetelmiä

	Monimuuttujaiset adaptiiviset regressiosplinit (MARS)	Päätöspuu
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1991	1984
Kehittäjä	Jerome H. Friedman	Breiman, Friedman, Olshen & Stone
Tyyppi	Adaptive piecewise-linear regression	Recursive partitioning (if-then rules)
Alkuperäislähde	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Rinnakkaisnimet	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liittyvät	4	5
Tiivistelmä	Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the 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.