Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

Abstract 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. T...

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Bibliographic Details
Main Authors: Burcu F. Darst, Kristen C. Malecki, Corinne D. Engelman
Format: Article
Language:English
Published: BMC 2018-09-01
Series:BMC Genetics
Subjects:
Genomics
Genetics
Epigenomics
Methylation
Machine-learning
Omics
Online Access:http://link.springer.com/article/10.1186/s12863-018-0633-8

Internet

http://link.springer.com/article/10.1186/s12863-018-0633-8

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