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

Burcu F. Darst; Kristen Malecki; Corinne D. Engelman

doi:10.1186/s12863-018-0633-8

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

Burcu F. Darst Kristen Malecki Corinne D. Engelman

Year: 2018 Journal: BMC Genetics Vol: 19 (S1)Pages: 65-65

DOI: 10.1186/s12863-018-0633-8

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Keywords:

Random forest Feature (linguistics) Correlation High dimensional Statistics Variables Mathematics Computer science Psychology Econometrics Artificial intelligence

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Genetic Associations and Epidemiology

Life Sciences → Biochemistry, Genetics and Molecular Biology → Genetics

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Bioinformatics and Genomic Networks

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

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

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