  Machine learning in high-stakes domains, such as healthcare, faces two
critical challenges: (1) generalizing to diverse data distributions given
limited training data while (2) maintaining interpretability. To address these
challenges, we propose an instance-weighted tree-sum method that effectively
pools data across diverse groups to output a concise, rule-based model. Given
distinct groups of instances in a dataset (e.g., medical patients grouped by
age or treatment site), our method first estimates group membership
probabilities for each instance. Then, it uses these estimates as instance
weights in FIGS (Tan et al. 2022), to grow a set of decision trees whose values
sum to the final prediction. We call this new method Group Probability-Weighted
Tree Sums (G-FIGS). G-FIGS achieves state-of-the-art prediction performance on
important clinical datasets; e.g., holding the level of sensitivity fixed at
92%, G-FIGS increases specificity for identifying cervical spine injury by up
to 10% over CART and up to 3% over FIGS alone, with larger gains at higher
sensitivity levels. By keeping the total number of rules below 16 in FIGS, the
final models remain interpretable, and we find that their rules match medical
domain expertise. All code, data, and models are released on Github.

Machine Learning;Artificial Intelligence;Applications;Methodology;Machine Learning