# Assessing model calibration with boosting trees

arXiv: https://arxiv.org/abs/2606.08084 · HTML: https://mlsanity.com/paper/2606.08084/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Regression Diagnostics meets Forecast Evaluation: Conditional Calibration, Reliability Diagrams, and Coefficient of Determination](https://mlsanity.com/paper/2108.03210/) (2021)
- [Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning](https://mlsanity.com/paper/2103.03635/) (2021)
- [Generic Conditions for Forecast Dominance](https://mlsanity.com/paper/1805.09902/) (2018)
- [Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice](https://mlsanity.com/paper/2202.12780/) (2022)
- [The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation](https://mlsanity.com/paper/2005.01835/) (2020)
- [Isotonic Recalibration under a Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2301.02692/) (2023)
- [A safe Hosmer-Lemeshow test](https://mlsanity.com/paper/2203.00426/) (2022)
- [Calibration Bands for Mean Estimates within the Exponential Dispersion Family](https://mlsanity.com/paper/2503.18896/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Practical Guide on Graphical Model Validation](https://mlsanity.com/paper/2609.26445/) (2026)

## Abstract (arXiv)

The main goal in regression modelling consists in approximating the conditional mean of a response given a set of features. A regression function is said to be calibrated if the resulting mean estimates match the true conditional means for almost every set of features. Aiming for calibration seems not achievable in practice as one typically deals with finite samples of noisy observations. A weaker notion of calibration is auto-calibration, and it means that the expectation of responses being given the same mean estimate matches this estimate. This notion is important, e.g., in insurance pricing as it ensures no cross-subsidization between different price cohorts. In this paper, we show that boosting trees can be used to test necessary conditions for calibration and auto-calibration, respectively. The practical relevance of our approach is supported by a numerical example, in which the proposed tests prove to be very powerful on a large insurance dataset.