# Making Sense of Random Forest Probabilities: a Kernel Perspective

arXiv: https://arxiv.org/abs/1812.05792 · HTML: https://mlsanity.com/paper/1812.05792/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers](https://mlsanity.com/paper/2402.01502/) (2024)
- [Maximally Machine-Learnable Portfolios](https://mlsanity.com/paper/2306.05568/) (2023)
- [Revisiting Forest Proximities via Sparse Leaf-Incidence Kernels](https://mlsanity.com/paper/2601.02735/) (2026)

## Abstract (arXiv)

A random forest is a popular tool for estimating probabilities in machine learning classification tasks. However, the means by which this is accomplished is unprincipled: one simply counts the fraction of trees in a forest that vote for a certain class. In this paper, we forge a connection between random forests and kernel regression. This places random forest probability estimation on more sound statistical footing. As part of our investigation, we develop a model for the proximity kernel and relate it to the geometry and sparsity of the estimation problem. We also provide intuition and recommendations for tuning a random forest to improve its probability estimates.