Comparar métodos

	Regresión por Mínimos Cuadrados Parciales (PLS)	Regresión Lineal Múltiple
Campo	Aprendizaje automático	Estadística
Familia	Machine learning	Regression model
Año de origen	1975	1886
Autor original	Herman Wold; popularized by Svante Wold in chemometrics	Francis Galton; formalized by Karl Pearson
Tipo	Supervised latent-variable regression	Parametric linear model
Fuente seminal	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
Alias	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	MLR, OLS regression, multiple regression, linear regression with multiple predictors
Relacionados	3	8
Resumen	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.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.