Extracting Interpretable Decision Tree Ensemble from Random Forest
Bogdan Gulowaty, Michał Woźniak
Abstract
Machine learning predictive models are nowadays widely applied in various systems in both commercial and public areas. The need to understand and comprehend their behavior has arisen and was, through an extensive increase in research in Explainable AI (XAI), noticed in the last years. One of the problems in the aforementioned XAI spectrum is understanding the large rule sets, such as those mined from big datasets or induced through Random Forest algorithms. This work explores the possibility of tackling such an issue using multiple nonoverlapping decision trees created with incorporated knowledge provided by the set of rules. Random Forest is being used as a source of such rules. Performance evaluation conducted on commonly used datasets shows that such a model could be competitive with an equally deep Random Forest while providing explanations that can be adjusted and interpreted as a rule list.