Сравнение методов

	Мультивариантные адаптивные регрессионные сплайны (MARS)	Регрессионные и сглаживающие сплайны
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1991	1996
Автор метода	Jerome H. Friedman	Spline regression literature; P-splines by Eilers & Marx
Тип	Adaptive piecewise-linear regression	Piecewise-polynomial nonparametric regression
Основополагающий источник	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Другие названия	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	splines, cubic splines, natural splines, smoothing splines
Связанные	4	4
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.	Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.