Rule Ensemble

Rule ensembles are machine learning models combining multiple simple rules to achieve high predictive accuracy while maintaining interpretability, addressing the "black box" nature of many complex models. Current research focuses on optimizing rule selection algorithms, such as those employing gradient boosting or mixed-integer programming, to balance predictive power with the number and complexity of rules, often prioritizing "local interpretability" – the number of rules needed to explain individual predictions. This work is significant because it enhances the trustworthiness and usability of machine learning models in high-stakes applications like healthcare and finance where understanding model decisions is crucial.