Porovnat metody

	Regresní a vyhlazovací splajny	Lokální regrese LOESS / LOWESS	Mnohorozměrné adaptivní regresní spliny (MARS)
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1996	1979	1991
Tvůrce	Spline regression literature; P-splines by Eilers & Marx	William S. Cleveland	Jerome H. Friedman
Typ	Piecewise-polynomial nonparametric regression	Local nonparametric regression smoother	Adaptive piecewise-linear regression
Původní zdroj	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.
Další názvy	splines, cubic splines, natural splines, smoothing splines	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları
Příbuzné	4	3	4
Shrnutí	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.	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.	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.