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

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.

Keywords:

• Risk classification
• boosting
• gradient boosting
• regression trees
• cost-complexity pruning

Disclosure statement

No potential conflict of interest was reported by the author(s).