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Boosting on the responses with Tweedie loss functions

Author

Listed:
  • Denuit, Michel

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

  • Trufin, Julien

    (ULB)

  • Verdebout, Thomas

    (ULB)

Abstract

Hainaut et al. (2022) showed that boosting can be conducted directly on the response under Tweedie loss function and log-link, by adapting the weights at each iteration step. In this short note, we complete the results obtained in Hainaut et al. (2022) by showing that among the usual link functions, the log-link function is actually the only one for which boosting can be regarded as an iteratively re-weighted procedure applied to the original data.

Suggested Citation

  • Denuit, Michel & Trufin, Julien & Verdebout, Thomas, 2022. "Boosting on the responses with Tweedie loss functions," LIDAM Discussion Papers ISBA 2022039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2022039
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    References listed on IDEAS

    as
    1. 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).
    2. Hainaut, Donatien & Trufin, Julien & Denuit, Michel, 2022. "Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link," LIDAM Reprints ISBA 2022037, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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