Compară metode

	Regresia prin metoda celor mai mici pătrate parțiale (PLS)	Regresia Ridge
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1975	1970
Autorul original	Herman Wold; popularized by Svante Wold in chemometrics	Hoerl, A.E. & Kennard, R.W.
Tip	Supervised latent-variable regression	L2-regularized linear regression
Sursa seminală	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.
Denumiri alternative	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Înrudite	3	4
Rezumat	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.