Файл:Extremely randomized trees Geurts-mlj-advance.pdf

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Extremely_randomized_trees_Geurts-mlj-advance.pdf(0 × 0 пикселей, размер файла: 2,93 МБ, MIME-тип: application/pdf)

Pierre Geurts·Damien Ernst·Louis Wehenkel Received: 14 June 2005 / Revised: 29 October 2005 / Accepted: 15 November 2005 / Published online: 2 March 2006 Springer Science+Business Media, Inc. 2006

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. Abias/variance an alysis of the Extra-Trees algorithm is also provided as well as a geometrical and akernel characterization of the models induced.

Keywords Supervised learning . Decision and regression trees . Ensemble methods . Cut-point randomization . Bias/variance tradeoff . Kernel-based models

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