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

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  • Julie Huyghe
  • Julien Trufin
  • Michel Denuit

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.

Suggested Citation

  • Julie Huyghe & Julien Trufin & Michel Denuit, 2024. "Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2024(5), pages 417-439, May.
    • Handle: RePEc:taf:sactxx:v:2024:y:2024:i:5:p:417-439
    DOI: 10.1080/03461238.2023.2258135
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