# Confidence Intervals for the Generalisation Error of Random Forests

arXiv: https://arxiv.org/abs/2201.11210 · HTML: https://mlsanity.com/paper/2201.11210/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2022.

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

- [Prediction Error Estimation in Random Forests](https://mlsanity.com/paper/2309.00736/) (2023)
- [Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data](https://mlsanity.com/paper/2408.03138/) (2024)

## Abstract (arXiv)

Out-of-bag error is commonly used as an estimate of generalisation error in ensemble-based learning models such as random forests. We present confidence intervals for this quantity using the delta-method-after-bootstrap and the jackknife-after-bootstrap techniques. These methods do not require growing any additional trees. We show that these new confidence intervals have improved coverage properties over the naive confidence interval, in real and simulated examples.