Comparer des méthodes

	Régression par composantes principales (RCP)	Régression Ridge
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1982	1970
Auteur d'origine	Principal-component regression literature (Jolliffe and others)	Hoerl, A.E. & Kennard, R.W.
Type	Unsupervised dimension reduction + regression	L2-regularized linear regression
Source fondatrice	Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Alias	PCR, PCA regression, temel bileşenler regresyonu	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Apparentées	3	4
Résumé	Principal components regression first compresses a set of correlated predictors into a few principal components — the directions of greatest variance — and then regresses the response on those components. By discarding low-variance directions, PCR stabilizes estimation in the presence of multicollinearity and high dimensionality, at the cost of choosing components without reference to the response.	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.