Improving random forest predictions in small datasets from two-phase sampling designs

Abstract Background 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 rar...

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Autores principales: Sunwoo Han, Brian D. Williamson, Youyi Fong
Formato: article
Lenguaje:EN
Publicado: BMC 2021
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Acceso en línea:https://doaj.org/article/59888be2e459495c93e907d674a72e1a
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