Explaining Predictions by Characteristic Rules

Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the characteristic type of rule has not previously been used by techniques for explaining predictions. This repository contains the source code of CEGA (Characteristic Explanatory General Association rules), which employs association rule mining to aggregate multiple explanations generated by any standard local explanation technique into a set of characteristic rules.