Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users
In our study, all HPO algorithms resulted in similar gains in model performance relative to baseline models. This finding likely relates to our study dataset having a large sample size, a relatively small number of features, and a strong signal to noise ratio; and would likely apply to other datasets with similar characteristics.
Authors
- Jun Guan (ORCID: https://orcid.org/0000-0002-2453-9022)
- Christopher Meaney (ORCID: https://orcid.org/0000-0002-5429-5233)
- Xuesong Wang (ORCID: https://orcid.org/0000-0001-9961-0017)
- Thérèse A. Stukel (ORCID: https://orcid.org/0000-0001-9283-8764)
Institutions
- Institute for Clinical Evaluative Sciences (CA)
- Institute for Work & Health (CA)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2025-05-15
- DOI
- https://doi.org/10.1186/s12874-025-02561-x
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 14.89