Title: Global Model Interpretation via Recursive Partitioning,
Abstract: In this work, we propose a simple but effective method to interpret black-box
machine learning models globally. That is, we use a compact binary tree, the
interpretation tree, to explicitly represent the most important decision rules
that are implicitly contained in the black-box machine learning models. This
tree is learned from the contribution matrix which consists of the
contributions of input variables to predicted scores for each single
prediction. To generate the interpretation tree, a unified process recursively
partitions the input variable space by maximizing the difference in the average
contribution of the split variable between the divided spaces. We demonstrate
the effectiveness of our method in diagnosing machine learning models on
multiple tasks. Also, it is useful for new knowledge discovery as such insights
are not easily identifiable when only looking at single predictions. In
general, our work makes it easier and more efficient for human beings to
understand machine learning models.