Assessing model fit by cross-validation

Douglas M Hawkins, Subhash C Basak, Denise Mills

Research output: Contribution to journalArticlepeer-review

539 Scopus citations

Abstract

When QSAR models are fitted, it is important to validate any fitted model - to check that it is plausible that its predictions will carry over to fresh data not used in the model fitting exercise. There are two standard ways of doing this - using a separate hold-out test sample and the computationally much more burdensome leave-one-out cross-validation in which the entire pool of available compounds is used both to fit the model and to assess its validity. We show by theoretical argument and empiric study of a large QSAR data set that when the available sample size is small - in the dozens or scores rather than the hundreds, holding a portion of it back for testing is wasteful, and that it is much better to use cross-validation, but ensure that this is done properly.

Original languageEnglish (US)
Pages (from-to)579-586
Number of pages8
JournalJournal of chemical information and computer sciences
Volume43
Issue number2
DOIs
StatePublished - Mar 1 2003

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