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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Opis bibliograficzny
Główni autorzy: Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel
Format: Artigo
Język:Inglês
Wydane: BMC 2025-05-01
Seria:BMC Medical Research Methodology
Hasła przedmiotowe:
Supervised machine learning
Clinical predictive modelling
Prediction model
Hyper-parameter optimization (HPO)
Hyper-parameter tuning (HPT)
Extreme gradient boosting classifier
Dostęp online:https://doi.org/10.1186/s12874-025-02561-x
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