Unceta, IreneNin, JordiPujol Vila, Oriol2019-03-142019-03-142018-11-23https://hdl.handle.net/2445/130337In 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.5 p.application/pdfeng(c) Unceta et al., 2018Risc de crèditHipotequesCredit riskMortgagesinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess