  Ensemble trees are a popular machine learning model which often yields high
prediction performance when analysing structured data. Although individual
small decision trees are deemed explainable by nature, an ensemble of large
trees is often difficult to understand. In this work, we propose an approach
called optimised explanation (OptExplain) that faithfully extracts global
explanations of ensemble trees using a combination of logical reasoning,
sampling and optimisation. Building on top of this, we propose a method called
the profile of equivalent classes (ProClass), which uses MAX-SAT to simplify
the explanation even further. Our experimental study on several datasets shows
that our approach can provide high-quality explanations to large ensemble trees
models, and it betters recent top-performers.
