Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistency of random forests   Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45
(2001) 5--32] that combines several randomized decision trees and aggregates
their predictions by averaging. Despite its wide usage and outstanding
practical performance, little is known about the mathematical properties of the
procedure. This disparity between theory and practice originates in the
difficulty to simultaneously analyze both the randomization process and the
highly data-dependent tree structure. In the present paper, we take a step
forward in forest exploration by proving a consistency result for Breiman's
[Mach. Learn. 45 (2001) 5--32] original algorithm in the context of additive
regression models. Our analysis also sheds an interesting light on how random
forests can nicely adapt to sparsity. 1. Introduction. Random forests are an
ensemble learning method for classification and regression that constructs a
number of randomized decision trees during the training phase and predicts by
averaging the results. Since its publication in the seminal paper of Breiman
(2001), the procedure has become a major data analysis tool, that performs well
in practice in comparison with many standard methods. What has greatly
contributed to the popularity of forests is the fact that they can be applied
to a wide range of prediction problems and have few parameters to tune. Aside
from being simple to use, the method is generally recognized for its accuracy
and its ability to deal with small sample sizes, high-dimensional feature
spaces and complex data structures. The random forest methodology has been
successfully involved in many practical problems, including air quality
prediction (winning code of the EMC data science global hackathon in 2012, see
http://www.kaggle.com/c/dsg-hackathon), chemoinformatics [Svetnik et al.
(2003)], ecology [Prasad, Iverson and Liaw (2006), Cutler et al. (2007)], 3D

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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cs
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio