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ML
2006
ACM

Extremely randomized trees

13 years 12 months ago
Extremely randomized trees
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 learning . Decision and regression trees . Ensemble methods . Cut-point randomization . Bias/variance tradeoff . Kernel-bas...
Pierre Geurts, Damien Ernst, Louis Wehenkel
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where ML
Authors Pierre Geurts, Damien Ernst, Louis Wehenkel
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