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

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 <i>C</i>-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; random forests and bagging did not achieve the target MAPE even with a 12-fold increase. For a neutral DGM that did not match any of the analysis models, logistic regression with only main effects and boosting resulted in target MAPE values with a 12-fold increase in the recommended sample size. For external validation, our simulations showed that sample size guidelines to achieve a target precision of the estimated <i>C</i>-statistic were suitable, and thus may be used to inform sample size calculations for MLTs.