Making Sense of Random Forest Probabilities: a Kernel Perspective
Machine Learning
2018-12-17 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1812.05792,
title = {Making Sense of Random Forest Probabilities: a Kernel Perspective},
author = {Matthew A. Olson and Abraham J. Wyner},
journal= {arXiv preprint arXiv:1812.05792},
year = {2018}
}