Сравнение на методи

	LOESS / LOWESS Локална регресия	Многомерни адаптивни регресионни сплайнове (MARS)
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1979	1991
Създател	William S. Cleveland	Jerome H. Friedman
Тип	Local nonparametric regression smoother	Adaptive piecewise-linear regression
Основополагащ източник	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.
Други названия	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları
Свързани	3	4
Резюме	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.