Improved high-dimensional prediction with Random Forests by the use of co-data

BACKGROUND: Prediction in high dimensional settings is difficult due to the large number of variables relative to the sample size. We demonstrate how auxiliary ‘co-data’ can be used to improve the performance of a Random Forest in such a setting. RESULTS: Co-data are incorporated in the Random Fores...

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Detalles Bibliográficos
Autores principales: te Beest, Dennis E., Mes, Steven W., Wilting, Saskia M., Brakenhoff, Ruud H., van de Wiel, Mark A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745983/
https://www.ncbi.nlm.nih.gov/pubmed/29281963
http://dx.doi.org/10.1186/s12859-017-1993-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5745983/
https://www.ncbi.nlm.nih.gov/pubmed/29281963
http://dx.doi.org/10.1186/s12859-017-1993-1

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