Making Sense of Random Forest Probabilities: a Kernel Perspective

Matthew A. Olson; Abraham J. Wyner

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arXiv · 1812.05792

Making Sense of Random Forest Probabilities: a Kernel Perspective

Matthew A. Olson·Abraham J. Wyner

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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.

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Matthew A. Olson, Abraham J. Wyner. 2018-12-14. Making Sense of Random Forest Probabilities: a Kernel Perspective. https://arxiv.org/abs/1812.05792

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