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This paper introduces RCProb, a probabilistic reformulation of the RuleCOSI+ algorithm for extracting decision rules from tree ensembles. RCProb reduces computational cost by estimating rule statistics using Dirichlet-smoothed class priors and Beta-smoothed condition likelihoods in a Naive Bayes framework, eliminating the need for repeated dataset scans. Experiments on 33 datasets demonstrate that RCProb achieves competitive predictive performance with a 22x runtime reduction and generates more compact rule sets compared to RuleCOSI+.
Rule extraction from tree ensembles just got 22x faster, without sacrificing accuracy or interpretability.
Tree ensembles are widely used in industrial machine learning due to their strong predictive performance and efficient training procedures. However, as the number of trees in an ensemble grows, the resulting models become increasingly difficult for humans to interpret. To address this limitation, explainable artificial intelligence (XAI) studies methods that generate interpretable models capable of explaining complex predictors. One approach consists of extracting decision rules from tree ensembles while attempting to preserve the predictive performance of the original model. In previous work, we introduced RuleCOSI+, a greedy heuristic algorithm for extracting compact rule-based models from tree ensembles. Although RuleCOSI+ produces accurate and interpretable rule sets, it relies on repeated empirical frequency counting over the training data to estimate rule confidence, which becomes computationally expensive for large datasets. In this paper, we propose RCProb, a probabilistic reformulation of RuleCOSI+ designed to reduce the computational cost of rule extraction. RCProb estimates rule statistics using Dirichlet-smoothed class priors and Beta-smoothed condition likelihoods combined through a Naive Bayes formulation, avoiding repeated dataset scans. Experiments on 33 benchmark datasets show that RCProb maintains competitive predictive performance while reducing runtime by approximately $22\times$ compared with RuleCOSI+, while producing more compact rule sets on average.
