Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models

arXiv:2403.17588v1 Announce Type: new
Abstract: Random Forest (RF) is well-known as an efficient ensemble learning method in terms of predictive performance. It is also considered a Black Box because of its hundreds of deep decision trees. This lack of interpretability can be a real drawback for acceptance of RF models in several real-world applications, especially those affecting one's lives, such as in healthcare, security, and law. In this work, we present Forest-ORE, a method that makes RF interpretable via an …

abstract arxiv black box box cs.lg decision decision trees ensemble interpretability mining performance predictive random terms trees type