Narrowing the Gap: Random Forests in Theory and in Practice
Abstract
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 theoreti- cally tractable variant of random regression forests and prove that our algorithm is con- sistent. We also provide an empirical eval- uation, comparing our algorithm and other theoretically tractable random forest models to the random forest algorithm used in prac- tice. Our experiments provide insight into the relative importance of different simplifi- cations that theoreticians have made to ob- tain tractable models for analysis.
Cite
Text
Denil et al. "Narrowing the Gap: Random Forests in Theory and in Practice." International Conference on Machine Learning, 2014.Markdown
[Denil et al. "Narrowing the Gap: Random Forests in Theory and in Practice." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/denil2014icml-narrowing/)BibTeX
@inproceedings{denil2014icml-narrowing,
title = {{Narrowing the Gap: Random Forests in Theory and in Practice}},
author = {Denil, Misha and Matheson, David and De Freitas, Nando},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {665-673},
volume = {32},
url = {https://mlanthology.org/icml/2014/denil2014icml-narrowing/}
}