  Tree-ensemble algorithms, such as random forest, are effective machine
learning methods popular for their flexibility, high performance, and
robustness to overfitting. However, since multiple learners are combined, they
are not as interpretable as a single decision tree. In this work we propose a
novel method that is Building Explanations through a LocalLy AccuraTe Rule
EXtractor (Bellatrex), and is able to explain the forest prediction for a given
test instance with only a few diverse rules. Starting from the decision trees
generated by a random forest, our method 1) pre-selects a subset of the rules
used to make the prediction, 2) creates a vector representation of such rules,
3) projects them to a low-dimensional space, 4) clusters such representations
to pick a rule from each cluster to explain the instance prediction. We test
the effectiveness of Bellatrex on 89 real-world datasets and we demonstrate the
validity of our method for binary classification, regression, multi-label
classification and time-to-event tasks. To the best of our knowledge, it is the
first time that an interpretability toolbox can handle all these tasks within
the same framework. We also show that our extracted surrogate model can
approximate the performance of the corresponding ensemble model in all
considered tasks, while selecting only few trees from the whole forest. We also
show that our proposed approach substantially outperforms other explainable
methods in terms of predictive performance.
