Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking

Julie Huyghe, Julien Trufin and Michel Denuit
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Julie Huyghe: Université Libre de Bruxelles
Julien Trufin: Université Libre de Bruxelles
Michel Denuit: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2024014, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: This paper proposes a new boosting machine based on forward stagewise additive modeling with cost-complexity pruned trees. In the Tweedie case, it deals directly with observed responses, not gradients of the loss function. Trees included in the score progressively reduce to the root-node one, in an adaptive way. The proposed Adaptive Boosting Tree (ABT) machine thus automatically stops at that time, avoiding to resort to the time-consuming cross validation approach. Case studies performed on motor third-party liability insurance claim data demonstrate the performances of the proposed ABT machine for ratemaking, in comparison with regular gradient boosting trees.

Keywords: Risk classification; boosting; gradient boosting; regression trees; cost-complexity pruning (search for similar items in EconPapers)
Pages: 23
Date: 2024-04-26
Note: In: Scandinavian Actuarial Journal, 2024, vol. 2024 (5), p. 417-439
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2024014

DOI: 10.1080/03461238.2023.2258135

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