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dc.contributor.authorRedelmeier, Annabelle Alice
dc.contributor.authorJullum, Martin
dc.contributor.authorAas, Kjersti
dc.date.accessioned2021-03-02T07:02:44Z
dc.date.available2021-03-02T07:02:44Z
dc.date.created2021-03-01T11:48:07Z
dc.date.issued2002
dc.identifier.urihttps://hdl.handle.net/11250/2731037
dc.description.abstractIt is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from any type of machine learning model. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. This methodology was then extended to explain dependent features with an underlying continuous distribution. In this paper, we propose a method to explain mixed (i.e. continuous, discrete, ordinal, and categorical) dependent features by modeling the dependence structure of the features using conditional inference trees. We demonstrate our proposed method against the current industry standards in various simulation studies and find that our method often outperforms the other approaches. Finally, we apply our method to a real financial data set used in the 2018 FICO Explainable Machine Learning Challenge and show how our explanations compare to the FICO challenge Recognition Award winning team.
dc.language.isoengen_US
dc.relation.ispartofLecture Notes in Computer Science
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.no*
dc.titleExplaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Treesen_US
dc.typeChapteren_US
dc.description.versionacceptedVersion
dc.description.versionacceptedVersion
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextpostprint
dc.identifier.cristin1894489
dc.source.pagenumber117-137en_US
dc.relation.projectNorges forskningsråd: 237718


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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
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