Comparer des méthodes

	Régression polynomiale	Régression Ridge
Domaine	Statistique	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	2012	1970
Auteur d'origine	Montgomery, Peck & Vining (textbook treatment); classical least squares	Hoerl, A.E. & Kennard, R.W.
Type	Linear regression in transformed predictors	L2-regularized linear regression
Source fondatrice	Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	polynomial least squares, curvilinear regression, Polinom Regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	4	4
Résumé	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.	Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated.