# Narrowing the Gap: Random Forests In Theory and In Practice

arXiv: https://arxiv.org/abs/1310.1415 · HTML: https://mlsanity.com/paper/1310.1415/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2013.

## Built on by (most cited first, 17 of 17)

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- [Autoencoding Random Forests](https://mlsanity.com/paper/2505.21441/) (2025)
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- [Data-driven multinomial random forest](https://mlsanity.com/paper/2304.04240/) (2023)
- [Generalized Random Forests using Fixed-Point Trees](https://mlsanity.com/paper/2306.11908/) (2023)
- [The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest](https://mlsanity.com/paper/2407.05108/) (2024)
- [Targeted tuning of random forests for quantile estimation and prediction intervals](https://mlsanity.com/paper/2507.01430/) (2025)
- [Covariance-Driven Regression Trees: Reducing Overfitting in CART](https://mlsanity.com/paper/2601.07281/) (2026)

## Abstract (arXiv)

Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of random regression forests and prove that our algorithm is consistent. We also provide an empirical evaluation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in practice. Our experiments provide insight into the relative importance of different simplifications that theoreticians have made to obtain tractable models for analysis.