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Cross-validation pitfalls when selecting and assessing regression and classification models
BACKGROUND: We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe...
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| Main Authors: | , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer International Publishing
2014
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3994246/ https://ncbi.nlm.nih.gov/pubmed/24678909 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1758-2946-6-10 |
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