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

Recurso electrónico / Electronic resource
MARC record
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040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
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1001 ‎$0‎MAPA20220002462‎$a‎Maillart, Arthur
24510‎$a‎Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data‎$c‎Arthur Maillart
520  ‎$a‎In 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
650 4‎$0‎MAPA20170005476‎$a‎Machine learning
650 4‎$0‎MAPA20080603779‎$a‎Seguro de automóviles
650 4‎$0‎MAPA20080556730‎$a‎Telemática
650  ‎$0‎MAPA20220007825‎$a‎Data driven
7730 ‎$w‎MAP20220007085‎$g‎06/12/2021 Volúmen 11 - Número 2 - diciembre 2021 , p. 579-617‎$t‎European Actuarial Journal‎$d‎Cham, Switzerland : Springer Nature Switzerland AG, 2021-2022