Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data
Contenido multimedia no disponible por derechos de autor o por acceso restringido. Contacte con la institución para más información.
| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20220008020 | ||
| 003 | MAP | ||
| 005 | 20220911210945.0 | ||
| 008 | 220310e20211206esp|||p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | 1 | $0MAPA20220002462$aMaillart, Arthur | |
| 245 | 1 | 0 | $aToward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data$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 | ||
| 773 | 0 | $wMAP20220007085$g06/12/2021 Volúmen 11 - Número 2 - diciembre 2021 , p. 579-617$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |