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

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

No 2021015, LIDAM Discussion Papers 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 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.

Keywords: Risk classification; Boosting; Gradient Boosting; Regression Trees; Cost-complexity pruning (search for similar items in EconPapers)
Pages: 17
Date: 2021-03-09
New Economics Papers: this item is included in nep-big and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2021015

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