Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users

Abstract Background Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive performance. Objective To compare several (...

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Detalhes bibliográficos

Principais autores:	Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2025-05-01
coleção:	BMC Medical Research Methodology
Assuntos:	Supervised machine learning Clinical predictive modelling Prediction model Hyper-parameter optimization (HPO) Hyper-parameter tuning (HPT) Extreme gradient boosting classifier
Acesso em linha:	https://doi.org/10.1186/s12874-025-02561-x
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