| Summary: | Although machine learning models are widely used in critical domains, their complexity and poor interpretability remain problematic. Decision trees (DTs) and rule-based models are known for their interpretability, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets, even though these approximators often overlook interpretability. These methods generate three types of rule sets: DT based, unordered, and decision list based. However, very few metrics exist that can distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow for comparisons of interpretability between different rule sets and investigates the interpretability of the rules generated by the tree ensemble approximators. We compare these rule sets with the <i>Recursive-Rule eXtraction algorithm (Re-RX) with J48graft</i> to offer insights into the interpretability gap. The results indicate that <i>Re-RX with J48graft</i> can handle categorical and numerical attributes separately, has simple rules, and achieves a high interpretability, even when the number of rules is large. <i>RuleCOSI+</i>, a state-of-the-art method, showed significantly lower results regarding interpretability, but had the smallest number of rules.
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