手法を比較

	回帰スプラインと平滑化スプライン	局所回帰 LOESS / LOWESS
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1996	1979
提唱者	Spline regression literature; P-splines by Eilers & Marx	William S. Cleveland
種類	Piecewise-polynomial nonparametric regression	Local nonparametric regression smoother
原典	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.
別名	splines, cubic splines, natural splines, smoothing splines	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon
関連	4	3
概要	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.