RCProb uses Dirichlet-smoothed class priors and Beta-smoothed condition likelihoods in a Naive Bayes formulation to extract rules from tree ensembles approximately 22 times faster than RuleCOSI+ while maintaining competitive accuracy and producing more compact rule sets on 33 benchmark datasets.
RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifiers.IEEE Transactions on Visualization and Computer Graphics, 31(9):6370–6384, September 2025
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RCProb: Probabilistic Rule Extraction for Efficient Simplification of Tree Ensembles
RCProb uses Dirichlet-smoothed class priors and Beta-smoothed condition likelihoods in a Naive Bayes formulation to extract rules from tree ensembles approximately 22 times faster than RuleCOSI+ while maintaining competitive accuracy and producing more compact rule sets on 33 benchmark datasets.