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 language | English (US) |
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Pages (from-to) | 579-586 |
Number of pages | 8 |
Journal | Journal of chemical information and computer sciences |
Volume | 43 |
Issue number | 2 |
DOIs | |
State | Published - Mar 2003 |