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

Journal: BMC medical research methodology
PMID: 40375157

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.

Authors

  • Christopher Meaney
    Department of Family and Community Medicine, University of Toronto, Toronto, Canada. christopher.meaney@utoronto.ca.
  • XueSong Wang
  • Jun Guan
    ICES, Toronto, Canada.
  • Therese A Stukel
    Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada.

Keywords

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