Title:
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Towards Global Explanations for Credit Risk Scoring
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Author:
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Unceta, Irene; Nin, Jordi; Pujol Vila, Oriol
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Abstract:
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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. |
Subject(s):
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-Risc de crèdit -Hipoteques -Credit risk -Mortgages |
Rights:
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(c) Unceta et al., 2018
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Document type:
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Conference Object |
Published by:
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Neural Information Processing Systems Foundation
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