New Method for Testing Model Calibration in Boosting Trees
A new paper on arXiv proposes using boosting trees as a statistical testing framework to assess calibration and auto-calibration in regression models. The work is motivated by insurance pricing applications, where auto-calibration ensures that mean predictions match true conditional means within price cohorts, preventing cross-subsidization. The method demonstrates strong statistical power on a large insurance dataset, offering a practical tool for validating predictive models in high-stakes domains.
Researchers have introduced a boosting-tree-based approach to test necessary conditions for both calibration and auto-calibration in regression models, according to a preprint submitted to arXiv. Calibration in regression refers to whether a model's predicted conditional means align with the true conditional means across feature sets, a property that is theoretically difficult to achieve with finite, noisy data. Auto-calibration is a weaker but practically important condition, requiring that the expected response among observations sharing the same predicted mean equals that prediction. This property is especially relevant in insurance pricing, where violations can lead to systematic cross-subsidization between different customer risk cohorts. The paper presents numerical experiments on a large insurance dataset, in which the proposed tests exhibit high statistical power. The 36-page manuscript covers statistical theory, machine learning methodology, and actuarial applications. As a preprint, the work has not yet undergone formal peer review.
What's missing
As a preprint, this work has not undergone peer review, so the validity of the theoretical claims and the generalizability of the empirical results remain to be independently verified. The specific insurance dataset used is not publicly identified, which limits reproducibility assessment.
What different sources said
- arXiv stat.MLCenter
Assessing model calibration with boosting trees
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