Histogram-based Probabilistic Rule Lists for Numeric Targets (short paper)

While most rule learning methods focus on categorical targets for tasks including classification and subgroup discovery, rule learning for numeric targets are under-studied, especially using probabilistic rules: the only existing probabilistic rule list method for numeric targets, named SSD ++ , is based on Gaussian parametric models. To extend this method, we adopt an adaptive histogram model to estimate the probability distribution of the target variable. We formalize the rule list learning as a model selection problem, which we tackle with the minimum description length principle. We demonstrate that our method produces rule lists with better predictive performance than SSD ++ .