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Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
BACKGROUND: Random forest (RF) is a machine-learning method that generally works well with high-dimensional problems and allows for nonlinear relationships between predictors; however, the presence of correlated predictors has been shown to impact its ability to identify strong predictors. The Rando...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2018
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157185/ https://www.ncbi.nlm.nih.gov/pubmed/30255764 http://dx.doi.org/10.1186/s12863-018-0633-8 |