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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Detalles Bibliográficos
Autores principales: Krstajic, Damjan, Buturovic, Ljubomir J, Leahy, David E, Thomas, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3994246/
https://www.ncbi.nlm.nih.gov/pubmed/24678909
http://dx.doi.org/10.1186/1758-2946-6-10

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3994246/
https://www.ncbi.nlm.nih.gov/pubmed/24678909
http://dx.doi.org/10.1186/1758-2946-6-10

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