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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Cite this study

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

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

Authors

Damjan Krstajić

C4X Discovery (United Kingdom)

Ljubomir Buturović

Infinera (United States)

David E. Leahy

MBDA (United Kingdom)

Journals

Journal of Cheminformatics

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Institutions

C4X Discovery (United Kingdom)

Redx Pharma (United Kingdom)

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Building similarity graph...

Analyzing shared references across papers

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

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Cite this study

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion