# Prediction Error Estimation in Random Forests

arXiv: https://arxiv.org/abs/2309.00736 · HTML: https://mlsanity.com/paper/2309.00736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 1 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Cross-validation: what does it estimate and how well does it do it?](https://mlsanity.com/paper/2104.00673/) (2021)
- [Confidence Intervals for the Generalisation Error of Random Forests](https://mlsanity.com/paper/2201.11210/) (2022)
- [Does Data Splitting Improve Prediction?](https://mlsanity.com/paper/1301.2983/) (2013)

## Abstract (arXiv)

In this paper, error estimates of classification Random Forests are quantitatively assessed. Based on the initial theoretical framework built by Bates et al. (2023), the true error rate and expected error rate are theoretically and empirically investigated in the context of a variety of error estimation methods common to Random Forests. We show that in the classification case, Random Forests' estimates of prediction error is closer on average to the true error rate instead of the average prediction error. This is opposite the findings of Bates et al. (2023) which are given for logistic regression. We further show that our result holds across different error estimation strategies such as cross-validation, bagging, and data splitting.