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Towards Global Explanations for Credit Risk Scoring

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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.

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Neural Information Processing Systems Foundation

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Comunicació a: NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montréal, Canada. December 7th, 2018

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Comunicacions a congressos  (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Towards Global Explanations for Credit Risk Scoring