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dc.contributor.advisorBorrás Pala, Francisco
dc.contributor.advisorCoronado Vaca, María
dc.contributor.authorStrasser, Luisa María
dc.contributor.otherUniversidad Pontificia Comillas, Facultad de Empresariales (ICADE)es_ES
dc.date.accessioned2024-01-24T09:11:52Z
dc.date.available2024-01-24T09:11:52Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/11531/86482
dc.descriptionGrado en Administración y Dirección de Empresas Mención Internacional (E-4)es_ES
dc.description.abstractIn the view of recent developments in Machine Learning techniques for Credit Scoring, a range of promising approaches have emerged. These advancements have challenged tradi-tional methods, such as the Logistic Regression model, and highlighted the effectiveness of ensemble tree-based models in achieving strong predictive outputs. However, it is crucial to consider factors beyond performance metrics, such as interpretability and business case criteria, when comparing these models. This thesis takes a comprehensive approach to evaluate selected tree-based models against the Logistic Regression across multiple evalua-tion metrics, including performance, interpretability and business case examination. Re-sults indicate that ensemble tree-based models outperform the Logistic Regression in all aspects, particularly in achieving higher recall values. Hence, it is suggested to explore newer Machine Learning methods as an alternative to traditional Credit Scoring tech-niques, depending on specific business requirements and data availability, with the aim of improving predictive performance and enabling more effective risk management in credit evaluation.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subject53 Ciencias económicases_ES
dc.subject5307 Teoría económicaes_ES
dc.subject530702 Teoría del créditoes_ES
dc.titleComparing ensemble tree-based machine lerarning mod-els to the regression for credit scorning – An emprircial analysis on home credit dataes_ES
dc.typeinfo:eu-repo/semantics/bachelorThesises_ES
dc.rights.accessRightsinfo:eu-repo/semantics/closedAccesses_ES


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