  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.
