手法を比較

	偏最小二乗回帰（PLS）	Multiple Linear Regression
分野	機械学習	統計学
系統	Machine learning	Regression model
提唱年	1975	1886
提唱者	Herman Wold; popularized by Svante Wold in chemometrics	Francis Galton; formalized by Karl Pearson
種類	Supervised latent-variable regression	Parametric linear model
原典	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.
別名	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler	MLR, OLS regression, multiple regression, linear regression with multiple predictors
関連	3	8
概要	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.