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Abstract Details
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Activity Number:
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223
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #300526 |
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Title:
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Random Forests
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Author(s):
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Gérard Biau*+
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Companies:
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Université Pierre et Marie Curie
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Address:
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Boîte 158, Tour 15-25, Paris, 75252, France
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Keywords:
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Random forests ;
Statistical learning ;
Sparsity ;
Regression estimation ;
Tree ;
Aggregation
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Abstract:
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Random forests are a scheme proposed by Leo Breiman in the 00's for building a predictor ensemble with a set of decision trees that grow in randomly selected subspaces of data. Despite growing interest and practical use, there has been little exploration of the statistical properties of random forests, and little is known about the mathematical forces driving the algorithm. In this talk, we offer an in-depth analysis of a random forests model suggested by Breiman in 04, which is very close to the original algorithm. We show in particular that the procedure is consistent and adapts to sparsity, in the sense that its rate of convergence depends only on the number of strong features and not on how many noise variables are present.
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Authors who are presenting talks have a * after their name.
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