From point to probabilistic gradient boosting for claim frequency and severity prediction
Contenido multimedia no disponible por derechos de autor o por acceso restringido. Contacte con la institución para más información.
| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260006222 | ||
| 003 | MAP | ||
| 005 | 20260310165937.0 | ||
| 008 | 260225e20261215che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20260002408$aChevalier, Dominik | ||
| 245 | 1 | 0 | $aFrom point to probabilistic gradient boosting for claim frequency and severity prediction$cDominik Chevalier and Marie-Pier Côté |
| 520 | $aGradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalised linear models. Many enhancements to the first gradient boosting machine algorithm exist. We present in a unified notation, and contrast, all the existing point and probabilistic gradient boosting for decision tree algorithms: GBM, XGBoost, DART, LightGBM, CatBoost, EGBM, PGBM, XGBoostLSS, cyclic GBM, and NGBoost. In this comprehensive numerical study, we compare their performance on five publicly available datasets for claim frequency and severity, of various sizes and comprising different numbers of (high cardinality) categorical variables. We explain how varying exposure-to-risk can be handled with boosting in frequency models. We compare the algorithms on the basis of computational efficiency, predictive performance, and model adequacy. LightGBM and XGBoostLSS win in terms of computational efficiency. CatBoost sometimes improves predictive performance, especially in the presence of high cardinality categorical variables, common in actuarial science. The fully interpretable EGBM achieves competitive predictive performance compared to the black box algorithms considered. We find that there is no trade-off between model adequacy and predictive accuracy: both are achievable simultaneously | ||
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080570651$aSiniestralidad | |
| 650 | 4 | $0MAPA20080592042$aModelos matemáticos | |
| 650 | 4 | $0MAPA20080564322$aTarificación | |
| 650 | 4 | $0MAPA20080611613$aModelos probabílisticos | |
| 700 | 1 | $0MAPA20260002415$aCôté, Marie-Pier | |
| 773 | 0 | $wMAP20220007085$g15/12/2025 Volume 15 Issue 3 - December 2025 , 46 p.$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |