# Targeted tuning of random forests for quantile estimation and prediction intervals

arXiv: https://arxiv.org/abs/2507.01430 · HTML: https://mlsanity.com/paper/2507.01430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success](https://mlsanity.com/paper/1911.00190/) (2019)
- [Narrowing the Gap: Random Forests In Theory and In Practice](https://mlsanity.com/paper/1310.1415/) (2013)
- [On the use of Harrell's C for clinical risk prediction via random survival forests](https://mlsanity.com/paper/1507.03092/) (2015)
- [Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest](https://mlsanity.com/paper/2103.16700/) (2021)
- [Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance](https://mlsanity.com/paper/2003.03629/) (2020)
- [Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation](https://mlsanity.com/paper/1801.07194/) (2018)
- [Extremal Random Forests](https://mlsanity.com/paper/2201.12865/) (2022)
- [Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests](https://mlsanity.com/paper/2408.07151/) (2024)

## Abstract (arXiv)

We present a novel tuning procedure for random forests (RFs) that improves the accuracy of estimated quantiles and produces valid, relatively narrow prediction intervals. While RFs are typically used to estimate mean responses (conditional on covariates), they can also be used to estimate quantiles by estimating the full distribution of the response. However, standard approaches for building RFs often result in excessively biased quantile estimates. To reduce this bias, our proposed tuning procedure minimizes "quantile coverage loss" (QCL), which we define as the estimated bias of the marginal quantile coverage probability estimate based on the out-of-bag sample. We adapt QCL tuning to handle censored data and demonstrate its use with random survival forests. We show that QCL tuning results in quantile estimates with more accurate coverage probabilities than those achieved using default parameter values or traditional tuning (using MSPE for uncensored data and C-index for censored data), while also reducing the estimated MSE of these coverage probabilities. We discuss how the superior performance of QCL tuning is linked to its alignment with the estimation goal. Finally, we explore the validity and width of prediction intervals created using this method.