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Predictive ability of regression models. Part II: Selection of the best predictive PLS model
Author(s) -
Baroni Massimo,
Clementi Sergio,
Cruciani Gabriele,
Costantino Gabriele,
Riganelli Daniela,
Oberrauch Ermanno
Publication year - 1992
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180060605
Subject(s) - predictive power , partial least squares regression , predictive modelling , selection (genetic algorithm) , regression analysis , feature selection , linear regression , regression , factorial experiment , factorial , computer science , statistics , mathematics , artificial intelligence , mathematical analysis , philosophy , epistemology
A procedure called GOLPE is suggested in order to detect those variables which increase the predictivity of PLS models. The procedure is based on evaluating the predictive power of a number of PLS models built by different combinations of variables selected according to a factorial design strategy. Examples are given of the efficiency of this variable selection procedure, which shows how these predictive PLS models are better than those obtained by all variables and better than the corresponding ordinary regression models.