Cross-study validation for the assessment of prediction algorithms

Motivation: Numerous competing algorithms for prediction in high-dimensional settings have been developed in the statistical and machine-learning literature. Learning algorithms and the prediction models they generate are typically evaluated on the basis of cross-validation error estimates in a few...

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Detalles Bibliográficos
Autores principales: Bernau, Christoph, Riester, Markus, Boulesteix, Anne-Laure, Parmigiani, Giovanni, Huttenhower, Curtis, Waldron, Levi, Trippa, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Ismb 2014 Proceedings Papers Committee
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4058929/
https://www.ncbi.nlm.nih.gov/pubmed/24931973
http://dx.doi.org/10.1093/bioinformatics/btu279

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4058929/
https://www.ncbi.nlm.nih.gov/pubmed/24931973
http://dx.doi.org/10.1093/bioinformatics/btu279

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