Making Tree Ensembles Interpretable
Machine Learning
2016-06-20 v1
Abstract
Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human. To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the complex ensemble. A synthetic experiment showed that a complicated tree ensemble was approximated reasonably as interpretable.
Cite
@article{arxiv.1606.05390,
title = {Making Tree Ensembles Interpretable},
author = {Satoshi Hara and Kohei Hayashi},
journal= {arXiv preprint arXiv:1606.05390},
year = {2016}
}
Comments
presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY