Methoden vergelijken

	Partiële Kleinste Kwadraten Regressie (PLS)	Ridge-regressie
Vakgebied	Machine learning	Machine learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	1975	1970
Grondlegger	Herman Wold; popularized by Svante Wold in chemometrics	Hoerl, A.E. & Kennard, R.W.
Type	Supervised latent-variable regression	L2-regularized linear regression
Oorspronkelijke bron	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.
Aliassen	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization
Verwant	3	4
Samenvatting	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.