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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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| Publicado no: | BMC Bioinformatics |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5745983/ https://ncbi.nlm.nih.gov/pubmed/29281963 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-017-1993-1 |
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