# Confidence intervals for the random forest generalization error

arXiv: https://arxiv.org/abs/2112.06101 · HTML: https://mlsanity.com/paper/2112.06101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Cross-validation Confidence Intervals for Test Error](https://mlsanity.com/paper/2007.12671/) (2020)

## Built on by (most cited first, 1 of 1)

- [Conformal prediction for frequency-severity modeling](https://mlsanity.com/paper/2307.13124/) (2023)

## Abstract (arXiv)

We show that the byproducts of the standard training process of a random forest yield not only the well known and almost computationally free out-of-bag point estimate of the model generalization error, but also give a direct path to compute confidence intervals for the generalization error which avoids processes of data splitting and model retraining. Besides the low computational cost involved in their construction, these confidence intervals are shown through simulations to have good coverage and appropriate shrinking rate of their width in terms of the training sample size.