방법 비교

	단계적 회귀분석	부분 최소 제곱 회귀 (PLS)
분야	통계학	머신러닝
계열	Regression model	Machine learning
기원 연도	1960	1975
창시자	M. A. Efroymson	Herman Wold; popularized by Svante Wold in chemometrics
유형	Automated variable selection	Supervised latent-variable regression
원전	Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.	Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130.
별칭	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection	PLS regression, projection to latent structures, PLSR, kısmi en küçük kareler
관련	5	3
요약	Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.	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.