Сравнение методов

	Регрессионные и сглаживающие сплайны	Мультивариантные адаптивные регрессионные сплайны (MARS)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	1996	1991
Автор метода	Spline regression literature; P-splines by Eilers & Marx	Jerome H. Friedman
Тип	Piecewise-polynomial nonparametric regression	Adaptive piecewise-linear regression
Основополагающий источник	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.
Другие названия	splines, cubic splines, natural splines, smoothing splines	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları
Связанные	4	4
Сводка	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.	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.