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Bias in error estimation when using cross-validation for model selection
BACKGROUND: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the...
| Autores principales: | , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2006
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1397873/ https://www.ncbi.nlm.nih.gov/pubmed/16504092 http://dx.doi.org/10.1186/1471-2105-7-91 |