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.
@article{arxiv.1811.07698,
title = {Towards Global Explanations for Credit Risk Scoring},
author = {Irene Unceta and Jordi Nin and Oriol Pujol},
journal= {arXiv preprint arXiv:1811.07698},
year = {2018}
}
Comments
FEAP-AI4Fin 2018 : NIPS 2018 Worksop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy