Journal Article

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

Stukel, Therese A., Guan, Jun, Meaney, Christopher, Wang, Xuesong

2025

Overview

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 (nine) hyper-parameter optimization (HPO) methods, for tuning the hyper-parameters of an extreme gradient boosting model, with application to predicting high-need high-cost health care users. Methods Extreme gradient boosting models were estimated using a randomly sampled training dataset. Models were separately trained using nine different HPO methods: 1) random sampling, 2) simulated annealing, 3) quasi-Monte Carlo sampling, 4-5) two variations of Bayesian hyper-parameter optimization via tree-Parzen estimation, 6-7) two implementations of Bayesian hyper-parameter optimization via Gaussian processes, 8) Bayesian hyper-parameter optimization via random forests, and 9) the covariance matrix adaptation evolutionary strategy. For each HPO method, we estimated 100 extreme gradient boosting models at different hyper-parameter configurations; and evaluated model performance using an AUC metric on a randomly sampled validation dataset. Using the best model identified by each HPO method, we evaluated generalization performance in terms of discrimination and calibration metrics on a randomly sampled held-out test dataset (internal validation) and a temporally independent dataset (external validation). Results The extreme gradient boosting model estimated using default hyper-parameter settings had reasonable discrimination (AUC=0.82) but was not well calibrated. Hyper-parameter tuning using any HPO algorithm/sampler improved model discrimination (AUC=0.84), resulted in models with near perfect calibration, and consistently identified features predictive of high-need high-cost health care users. Conclusions 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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Bayes Theorem / Clinical predictive modelling / Data mining / Datasets / Experiments / Extreme gradient boosting classifier / Health Care Costs - statistics & numerical data / Health Sciences / Health Services Needs and Demand - economics / Health Services Needs and Demand - statistics & numerical data / Humans / Hyper-parameter optimization (HPO) / Hyper-parameter tuning (HPT) / Machine Learning / Medical care, Cost of / Medical research / Medicine / Medicine & Public Health / Medicine, Experimental / Methods / Monte Carlo Method / Monte Carlo simulation / Optimization algorithms / Performance evaluation / Prediction model / Probability distribution / Sample size / Signal to noise ratio / Statistical Theory and Methods / Statistics for Life Sciences / Supervised machine learning / Theory of Medicine/Bioethics