Improving random forest predictions in small datasets from two-phase sampling designs
Improving random forest predictions in small datasets from two-phase sampling designs
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Publisher
England: BioMed Central Ltd
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English
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Publisher
England: BioMed Central Ltd
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Contents
While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases-a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive.
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Full title
Improving random forest predictions in small datasets from two-phase sampling designs
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TN_cdi_doaj_primary_oai_doaj_org_article_59888be2e459495c93e907d674a72e1a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_59888be2e459495c93e907d674a72e1a
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ISSN
1472-6947
E-ISSN
1472-6947
DOI
10.1186/s12911-021-01688-3
