DTRBench: A Benchmarking Framework for Decision Tree Representations

Due to their efficiency, interpretability, and ensemble capabilities, decision trees are foundational to machine learning, with recent benchmark studies showing that tree ensembles remain competitive with deep learning on tabular data. Yet, systematically comparing individual trees remains difficult due to structural and semantic variability. While decision tree representations enable pairwise comparisons, existing representation methods have only been developed in isolation on narrow tasks. Consequently, their utility and broader applicability cannot be judged. To address this gap, we present DTRBench, a modular benchmarking framework for evaluating decision tree representations across three axes: task-independent sensitivity to controlled perturbations, utility for downstream subforest selection, and computational efficiency. Across 25 datasets, we benchmark five representations from the literature, spanning vector-, graph-, and neural-based paradigms, alongside an illustrative new hybrid vector representation we call “Tree Descriptor”. The results show that local perturbation sensitivity does not translate to downstream subforest selection utility, highlighting the need for measuring them as decoupled properties. Furthermore, DTRBench reveals that subforest selection based on Tree Descriptor representations requires up to 73% fewer trees than standard heuristics to recover 99% of the full random forest's predictive performance, thereby substantially accelerating inference. Ultimately, DTRBench provides an extensible, standardized, and open-source foundation for evaluating and selecting decision tree representations, revealing distinct strengths and limitations across representations.