# Localized Uncertainty Quantification in Random Forests via Proximities

arXiv: https://arxiv.org/abs/2509.22928 · HTML: https://mlsanity.com/paper/2509.22928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife](https://mlsanity.com/paper/1311.4555/) (2013)
- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [Random Forest Missing Data Algorithms](https://mlsanity.com/paper/1701.05305/) (2017)
- [A Unified Framework for Random Forest Prediction Error Estimation](https://mlsanity.com/paper/1912.07435/) (2019)
- [Enhanced Local Explainability and Trust Scores with Random Forest Proximities](https://mlsanity.com/paper/2310.12428/) (2023)

## Abstract (arXiv)

In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasize predictive accuracy, but there is a growing focus on incorporating uncertainty measures. This paper addresses localized uncertainty quantification in random forests. While current methods often rely on quantile regression or Monte Carlo techniques, we propose a new approach using naturally occurring test sets and similarity measures (proximities) typically viewed as byproducts of random forests. Specifically, we form localized distributions of OOB errors around nearby points, defined using the proximities, to create prediction intervals for regression and trust scores for classification. By varying the number of nearby points, our intervals can be adjusted to achieve the desired coverage while retaining the flexibility that reflects the certainty of individual predictions. For classification, excluding points identified as unclassifiable by our method generally enhances the accuracy of the model and provides higher accuracy-rejection AUC scores than competing methods.