Machine Learning

, Volume 63, Issue 1, pp 3–42

Extremely randomized trees

Article

DOI: 10.1007/s10994-006-6226-1

Cite this article as:
Geurts, P., Ernst, D. & Wehenkel, L. Mach Learn (2006) 63: 3. doi:10.1007/s10994-006-6226-1

Abstract

This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced.

Keywords

Supervised learningDecision and regression treesEnsemble methodsCut-point randomizationBias/variance tradeoffKernel-based models
Download to read the full article text

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of LiègeLiègeBelgium