Сравнение на методи

	Многомерни адаптивни регресионни сплайнове (MARS)	Regression Splines
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1991	1996
Създател	Jerome H. Friedman	Spline regression literature; P-splines by Eilers & Marx
Тип	Adaptive piecewise-linear regression	Piecewise-polynomial nonparametric regression
Основополагащ източник	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.
Други названия	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	splines, cubic splines, natural splines, smoothing splines
Свързани	4	4
Резюме	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.