Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction.

ABSTRACT: Machine learning techniques (MLTs) are increasingly being used to develop clinical risk prediction models for binary health outcomes but the sample size requirements for developing and validating such models remain unclear. This study investigates whether sample size guidelines that target mean absolute prediction error (MAPE) for logistic regression models can be applied to tree-based ensemble MLTs (bagging, random forests, and boosting). Simulations based on two large cardiovascular datasets were used to evaluate the performance of MLTs in terms of MAPE, calibration, the C-statistic and Brier score, across six data-generating mechanisms (DGMs) and varying sample sizes. When the DGM and analysis model matched, boosting required a sample size 2-3 times larger than recommended; rando

SUBMITTER: Kalaycıoglu O 

PROVIDER: S-EPMC12308042 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature