Comparer des méthodes

	Régression Ridge	Analyse en composantes principales
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1970	2002
Auteur d'origine	Hoerl, A.E. & Kennard, R.W.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	L2-regularized linear regression	Unsupervised dimensionality reduction
Source fondatrice	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	4	3
Résumé	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.