Сравнение методов

	Мультивариантные адаптивные регрессионные сплайны (MARS)	Дерево решений
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1991	1984
Автор метода	Jerome H. Friedman	Breiman, Friedman, Olshen & Stone
Тип	Adaptive piecewise-linear regression	Recursive partitioning (if-then rules)
Основополагающий источник	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.
Другие названия	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Связанные	4	5
Сводка	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.