Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/130337
Title: | Towards Global Explanations for Credit Risk Scoring |
Author: | Unceta, Irene Nin, Jordi Pujol Vila, Oriol |
Keywords: | Risc de crèdit Hipoteques Credit risk Mortgages |
Issue Date: | 23-Nov-2018 |
Publisher: | Neural Information Processing Systems Foundation |
Abstract: | 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. |
It is part of: | 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 |
URI: | http://hdl.handle.net/2445/130337 |
Appears in Collections: | Comunicacions a congressos (Matemàtiques i Informàtica) |
Files in This Item:
File | Description | Size | Format | |
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UncetaNIPS18.pdf | 1.72 MB | Adobe PDF | View/Open |
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