# Probability Calibration Trees

arXiv: https://arxiv.org/abs/1808.00111 · HTML: https://mlsanity.com/paper/1808.00111/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

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

- [Classifier Calibration: A survey on how to assess and improve predicted class probabilities](https://mlsanity.com/paper/2112.10327/) (2021)
- [Using Platt's scaling for calibration after undersampling -- limitations and how to address them](https://mlsanity.com/paper/2410.18144/) (2024)
- [Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing](https://mlsanity.com/paper/2209.05486/) (2022)
- [Variable-Based Calibration for Machine Learning Classifiers](https://mlsanity.com/paper/2209.15154/) (2022)
- [Measuring Uncertainty Calibration](https://mlsanity.com/paper/2512.13872/) (2025)
- [Evidential Rule Learning for Interpretable Classification with Abstention](https://mlsanity.com/paper/2608.05859/) (2026)

## Abstract (arXiv)

Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Existing methods of calibrating probability estimates are applied globally, ignoring the potential for improvements by applying a more fine-grained model. We propose probability calibration trees, a modification of logistic model trees that identifies regions of the input space in which different probability calibration models are learned to improve performance. We compare probability calibration trees to two widely used calibration methods---isotonic regression and Platt scaling---and show that our method results in lower root mean squared error on average than both methods, for estimates produced by a variety of base learners.