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Boosting cost-complexity pruned trees On Tweedie responses: the ABT machine

Author

Listed:
  • 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 res-ponses, 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. A case study performed on motor third-party liability insurance claim data demons-trates the performances of the proposed ABT machine for ratemaking, in comparison with regu-lar gradient boosting trees.

Suggested Citation

  • Trufin, Julien & Denuit, Michel, 2021. "Boosting cost-complexity pruned trees On Tweedie responses: the ABT machine," LIDAM Discussion Papers ISBA 2021015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2021015
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    References listed on IDEAS

    as
    1. Yi Yang & Wei Qian & Hui Zou, 2018. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 456-470, July.
    2. Denuit, Michel & Hainaut, Donatien & Trufin, Julien, 2020. "Effective Statistical Learning Methods for Actuaries II : Tree-Based Methods and Extensions," LIDAM Reprints ISBA 2020035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Simon C. K. Lee & Sheldon Lin, 2018. "Delta Boosting Machine with Application to General Insurance," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(3), pages 405-425, July.
    4. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Risk classification ; Boosting ; Gradient Boosting ; Regression Trees ; Cost-complexity pruning;
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