Compara mètodes

	Splines de regressió i de suavització	Regressió polinòmica
Camp	Aprenentatge automàtic	Estadística
Família	Machine learning	Regression model
Any d'origen	1996	2012
Autor original	Spline regression literature; P-splines by Eilers & Marx	Montgomery, Peck & Vining (textbook treatment); classical least squares
Tipus	Piecewise-polynomial nonparametric regression	Linear regression in transformed predictors
Font seminal	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811
Àlies	splines, cubic splines, natural splines, smoothing splines	polynomial least squares, curvilinear regression, Polinom Regresyonu
Relacionats	4	4
Resum	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.	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.