So sánh phương pháp

	Hồi quy Spline và Spline Làm mịn	Hồi quy Spline Thích nghi Đa biến (MARS)	Hồi quy đa thức
Lĩnh vực	Học máy	Học máy	Thống kê
Họ	Machine learning	Machine learning	Regression model
Năm ra đời	1996	1991	2012
Người khởi xướng	Spline regression literature; P-splines by Eilers & Marx	Jerome H. Friedman	Montgomery, Peck & Vining (textbook treatment); classical least squares
Loại	Piecewise-polynomial nonparametric regression	Adaptive piecewise-linear regression	Linear regression in transformed predictors
Công trình gốc	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.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
Tên gọi khác	splines, cubic splines, natural splines, smoothing splines	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	polynomial least squares, curvilinear regression, Polinom Regresyonu
Liên quan	4	4	4
Tóm tắt	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.	Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.