Using recursive feature elimination in random forest to account for correlated variables in high dim...
Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
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Publisher
England: BioMed Central Ltd
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Language
English
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England: BioMed Central Ltd
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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 Random Forest-Recursive Feature Elimination algorithm (RF-RFE) miti...
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Full title
Using recursive feature elimination in random forest to account for correlated variables in high dimensional data
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TN_cdi_doaj_primary_oai_doaj_org_article_c9e7b48bfbc14afb95f76355f8eace7a
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c9e7b48bfbc14afb95f76355f8eace7a
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ISSN
1471-2156
E-ISSN
1471-2156
DOI
10.1186/s12863-018-0633-8
