Comparar métodos

	Regressão por Mínimos Quadrados Parciais (PLS)	Regressão Ridge
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1975	1970
Autor original	Herman Wold; popularized by Svante Wold in chemometrics	Hoerl, A.E. & Kennard, R.W.
Tipo	Supervised latent-variable regression	L2-regularized linear regression
Fonte seminal	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.	Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67.
Outros nomes	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Relacionados	3	4
Resumo	Partial least squares regression predicts a response from many, often highly collinear predictors by projecting them onto a small set of latent components — but, unlike principal components regression, it chooses those components to maximize their covariance with the response, not just the variance of the predictors. This supervised dimension reduction makes PLS a workhorse in chemometrics, spectroscopy, and other wide-data settings where predictors vastly outnumber observations.	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.