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Surrogate minimal depth as an importance measure for variables in random forests
MOTIVATION: It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor var...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761946/ https://www.ncbi.nlm.nih.gov/pubmed/30824905 http://dx.doi.org/10.1093/bioinformatics/btz149 |