mlsanityData to 7 Oct 2026

Model selection with Gini indices under auto-calibration

arXiv 2207.14372 ↗First posted 28 Jul 2022cs.LG stat.MLcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 2 of its 19 references that are in the map. First posted on arXiv 28 Jul 2022.

Builds on

2 of 19 references.

References · 2

  1. 2021Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learningcited by 18
  2. 2018Generic Conditions for Forecast Dominancecited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 1.

6 papers.

  1. 2023Isotonic Recalibration under a Low Signal-to-Noise Ratiocited by 7
  2. 2022A Discussion of Discrimination and Fairness in Insurance Pricingcited by 4
  3. 2025Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurancecited by 0
  4. 2025Model Monitoring: A General Framework with an Application to Non-life Insurance Pricingcited by 0
  5. 2025Gini Score under Ties and Case Weightscited by 0
  6. 2026CAIRO: Decoupling Order from Scale in Regressioncited by 0