Comparar métodos

	Regressão Ridge	Análise de Componentes Principais
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1970	2002
Autor original	Hoerl, A.E. & Kennard, R.W.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	L2-regularized linear regression	Unsupervised dimensionality reduction
Fonte seminal	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.
Outros nomes	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionados	4	3
Resumo	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.