Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validationReport as inadecuate

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Journal of Cheminformatics

, 6:47

First Online: 26 November 2014Received: 08 July 2014Accepted: 30 October 2014


BackgroundGenerally, QSAR modelling requires both model selection and validation since there is no a priori knowledge about the optimal QSAR model. Prediction errors PE are frequently used to select and to assess the models under study. Reliable estimation of prediction errors is challenging – especially under model uncertainty – and requires independent test objects. These test objects must not be involved in model building nor in model selection. Double cross-validation, sometimes also termed nested cross-validation, offers an attractive possibility to generate test data and to select QSAR models since it uses the data very efficiently. Nevertheless, there is a controversy in the literature with respect to the reliability of double cross-validation under model uncertainty. Moreover, systematic studies investigating the adequate parameterization of double cross-validation are still missing. Here, the cross-validation design in the inner loop and the influence of the test set size in the outer loop is systematically studied for regression models in combination with variable selection.

MethodsSimulated and real data are analysed with double cross-validation to identify important factors for the resulting model quality. For the simulated data, a bias-variance decomposition is provided.

ResultsThe prediction errors of QSAR-QSPR regression models in combination with variable selection depend to a large degree on the parameterization of double cross-validation. While the parameters for the inner loop of double cross-validation mainly influence bias and variance of the resulting models, the parameters for the outer loop mainly influence the variability of the resulting prediction error estimate.

ConclusionsDouble cross-validation reliably and unbiasedly estimates prediction errors under model uncertainty for regression models. As compared to a single test set, double cross-validation provided a more realistic picture of model quality and should be preferred over a single test set.

KeywordsCross-validation Double cross-validation Internal validation External validation Prediction error Regression Electronic supplementary materialThe online version of this article doi:10.1186-s13321-014-0047-1 contains supplementary material, which is available to authorized users.

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Author: Désirée Baumann - Knut Baumann


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