# Conformal prediction for frequency-severity modeling

arXiv: https://arxiv.org/abs/2307.13124 · HTML: https://mlsanity.com/paper/2307.13124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019)
- [Conformal Prediction: a Unified Review of Theory and New Challenges](https://mlsanity.com/paper/2005.07972/) (2020)
- [Boosting insights in insurance tariff plans with tree-based machine learning methods](https://mlsanity.com/paper/1904.10890/) (2019)
- [Universal distribution of the empirical coverage in split conformal prediction](https://mlsanity.com/paper/2303.02770/) (2023)
- [Confidence intervals for the random forest generalization error](https://mlsanity.com/paper/2112.06101/) (2021)

## Built on by (most cited first, 2 of 2)

- [Projected random forests and conformal prediction of circular data](https://mlsanity.com/paper/2410.24145/) (2024)
- [Skew-adaptive conformal prediction](https://mlsanity.com/paper/2605.16145/) (2026)

## Abstract (arXiv)

We present a model-agnostic framework for the construction of prediction intervals of insurance claims, with finite sample statistical guarantees, extending the technique of split conformal prediction to the domain of two-stage frequency-severity modeling. The framework effectiveness is showcased with simulated and real datasets using classical parametric models and contemporary machine learning methods. When the underlying severity model is a random forest, we extend the two-stage split conformal prediction algorithm, showing how the out-of-bag mechanism can be leveraged to eliminate the need for a calibration set in the conformal procedure.