Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data

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<dc:creator>Maillart, Arthur</dc:creator>
<dc:date>2021-12-06</dc:date>
<dc:description xml:lang="es">Sumario: In this paper, we suggest an explainable machine learning approach to model the claim frequency of a telematics car dataset. In fact, we use a data-driven method based on tree ensembles, namely, the random forest, to create a claim frequency model. Then, we present a method to build a tree that faithfully synthesizes the predictions of a tree ensemble model such as those derived from the random forest or gradient boosting. This tree serves as a global explanation of the predictions of the black-box. Thanks to this surrogate model, we can extract knowledge from a black-box tree ensemble model. Then, we provide an application to improve the performance of a generalized linear model. Indeed, we integrate this new knowledge into a generalized linear model to increase the predictive power</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/179015.do</dc:identifier>
<dc:language>spa</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Machine learning</dc:subject>
<dc:subject xml:lang="es">Seguro de automóviles</dc:subject>
<dc:subject xml:lang="es">Telemática</dc:subject>
<dc:subject xml:lang="es">Data driven</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data</dc:title>
<dc:relation xml:lang="es">En: European Actuarial Journal. - Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022. - 06/12/2021 Número 2 - diciembre 2021 , p. 579-617</dc:relation>
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