# A Practical Guide on Graphical Model Validation

arXiv: https://arxiv.org/abs/2609.26445 · HTML: https://mlsanity.com/paper/2609.26445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 61 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [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)
- [Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing](https://mlsanity.com/paper/2510.04556/) (2025)
- [Assessing model calibration with boosting trees](https://mlsanity.com/paper/2606.08084/) (2026)

## Abstract (arXiv)

This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like calibration plots, actual-vs-expected plots, lift charts, Murphy diagrams, as well as classical statistical tools such as Bregman losses, deviance losses, elementary losses, Murphy's decomposition and Gini scores. Particular emphasis is placed on whether calibration and discrimination are studied under a policy-weighted or an exposure-weighted population measure. This distinction is crucial in ensuring that premium schemes are calibrated on the correct scale.