Compara mètodes

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