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http://hdl.handle.net/2445/130337
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DC Field | Value | Language |
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dc.contributor.author | Unceta, Irene | - |
dc.contributor.author | Nin, Jordi | - |
dc.contributor.author | Pujol Vila, Oriol | - |
dc.date.accessioned | 2019-03-14T08:17:59Z | - |
dc.date.available | 2019-03-14T08:17:59Z | - |
dc.date.issued | 2018-11-23 | - |
dc.identifier.uri | http://hdl.handle.net/2445/130337 | - |
dc.description.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. | ca |
dc.format.extent | 5 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.publisher | Neural Information Processing Systems Foundation | ca |
dc.relation.ispartof | 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 | - |
dc.rights | (c) Unceta et al., 2018 | - |
dc.source | Comunicacions a congressos (Matemàtiques i Informàtica) | - |
dc.subject.classification | Risc de crèdit | - |
dc.subject.classification | Hipoteques | - |
dc.subject.other | Credit risk | - |
dc.subject.other | Mortgages | - |
dc.title | Towards Global Explanations for Credit Risk Scoring | ca |
dc.type | info:eu-repo/semantics/conferenceObject | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
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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