# Model selection with Gini indices under auto-calibration

arXiv: https://arxiv.org/abs/2207.14372 · HTML: https://mlsanity.com/paper/2207.14372/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [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)

## Built on by (most cited first, 6 of 6)

- [Isotonic Recalibration under a Low Signal-to-Noise Ratio](https://mlsanity.com/paper/2301.02692/) (2023)
- [A Discussion of Discrimination and Fairness in Insurance Pricing](https://mlsanity.com/paper/2209.00858/) (2022)
- [Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing](https://mlsanity.com/paper/2510.04556/) (2025)
- [Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance](https://mlsanity.com/paper/2503.21321/) (2025)
- [Gini Score under Ties and Case Weights](https://mlsanity.com/paper/2511.15446/) (2025)
- [CAIRO: Decoupling Order from Scale in Regression](https://mlsanity.com/paper/2602.14440/) (2026)

## Abstract (arXiv)

The Gini index does not give a strictly consistent scoring rule in general. Therefore, maximizing the Gini index may lead to wrong decisions. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for strictly consistent scoring if we restrict to the class of auto-calibrated regression models.