March 29, 2014Open Access

Cross-validation pitfalls when selecting and assessing regression and classification models

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Abstract

We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

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Krstajić et al. (Sat,) studied this question.

synapsesocial.com/papers/69d755dcb4cef8fedc48f657 https://doi.org/https://doi.org/10.1186/1758-2946-6-10

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Journals

Journal of Cheminformatics

Institutions

C4X Discovery (United Kingdom)

Redx Pharma (United Kingdom)

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Cross-validation pitfalls when selecting and assessing regression and classification models

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Abstract

Citation Network

Connected Papers

Cite This Study

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Discussion

Journals

Institutions

References and Citations

Also Consider