Comparer des méthodes

	Régression par moindres carrés partiels (PLS)	Régression par composantes principales (RCP)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1975	1982
Auteur d'origine	Herman Wold; popularized by Svante Wold in chemometrics	Principal-component regression literature (Jolliffe and others)
Type	Supervised latent-variable regression	Unsupervised dimension reduction + regression
Source fondatrice	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.	Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 31(3), 300–303.
Alias	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	PCR, PCA regression, temel bileşenler regresyonu
Apparentées	3	3
Résumé	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.	Principal components regression first compresses a set of correlated predictors into a few principal components — the directions of greatest variance — and then regresses the response on those components. By discarding low-variance directions, PCR stabilizes estimation in the presence of multicollinearity and high dimensionality, at the cost of choosing components without reference to the response.