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...

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Detalles Bibliográficos
Autores principales: Darst, Burcu F., Malecki, Kristen C., Engelman, Corinne D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research
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

Internet

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

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