| Issue |
ESAIM: Procs
Volume 60, 2017
Journées MAS 2016 de la SMAI – Phénomènes complexes et hétérogènes
|
|
|---|---|---|
| Page(s) | 144 - 162 | |
| DOI | https://doi.org/10.1051/proc/201760144 | |
| Published online | 14 December 2017 | |
Tuning parameters in random forests
CMAP, École Polytechnique, Route de Saclay, 91128 Palaiseau
Breiman's (2001) random forests are a very popular class of learning algorithms often able to produce good predictions even in high-dimensional frameworks, with no need to accurately tune its inner parameters. Unfortunately, there are no theoretical findings to support the default values used for these parameters in Breiman's algorithm. The aim of this paper is therefore to present recent theoretical results providing some insights on the role and the tuning of these parameters.
© EDP Sciences, SMAI 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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