# Consistency of Online Random Forests

arXiv: https://arxiv.org/abs/1302.4853 · HTML: https://mlsanity.com/paper/1302.4853/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2013.

## Built on by (most cited first, 4 of 4)

- [Random Forest Weighted Local Fr'echet Regression with Random Objects](https://mlsanity.com/paper/2202.04912/) (2022)
- [A model-free feature selection technique of feature screening and random forest based recursive feature elimination](https://mlsanity.com/paper/2302.07449/) (2023)
- [Time series quantile regression using random forests](https://mlsanity.com/paper/2211.02273/) (2022)
- [Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance](https://mlsanity.com/paper/2410.04297/) (2024)

## Abstract (arXiv)

As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.