  In this paper we propose a method to obtain global explanations for trained
black-box classifiers by sampling their decision function to learn alternative
interpretable models. The envisaged approach provides a unified solution to
approximate non-linear decision boundaries with simpler classifiers while
retaining the original classification accuracy. We use a private residential
mortgage default dataset as a use case to illustrate the feasibility of this
approach to ensure the decomposability of attributes during pre-processing.
