Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable

Ensembles of decision trees have good prediction accuracy but suffer from a lack of interpretability. We propose a new approach for interpreting tree ensembles by finding prototypes in tree space, utilizing the naturally-learned similarity measure from the tree ensemble. Demonstrating the method on random forests, we show that the method benefits from two unique aspects of tree ensembles by leveraging tree structure to sequentially find prototypes, and utilizing the naturally-learned similarity measure from the tree ensemble. The method provides good prediction accuracy when found prototypes are used in nearest-prototype classifiers, while using fewer prototypes than competitor methods. We are investigating the sensitivity of the method to different prototype-finding procedures and demonstrating it on higher-dimensional data.