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

Author

Listed:
  • Huyghe, Julie

    (Université Libre de Bruxelles)

  • Trufin, Julien

    (Université Libre de Bruxelles)

  • Denuit, Michel

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

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

  • Huyghe, Julie & Trufin, Julien & Denuit, Michel, 2024. "Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking," LIDAM Reprints ISBA 2024014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2024014
    DOI: https://doi.org/10.1080/03461238.2023.2258135
    Note: In: Scandinavian Actuarial Journal, 2024, vol. 2024 (5), p. 417-439
    as

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