Transcription of 1 RANDOM FORESTS
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1 RANDOM FORESTS
1 RANDOM FORESTS Leo BreimanStatistics Department University of California Berkeley, CA 94720 January 2001 AbstractRandom FORESTS are a combination of tree predictorssuch that each tree depends on the values of a randomvector sampled independently and with the samedistribution for all trees in the forest. Thegeneralization error for FORESTS converges to a limitas the number of trees in the forest becomes generalization error of a forest of tree classifiersdepends on the strength of the individual trees in theforest and the correlation between them. Using arandom selection of features to split each node yieldserror rates that compare favorably to Adaboost(Freund and Schapire[1996]), but are more robust withrespect to noise. Internal estimates monitor error,strength, and correlation and these are used to showthe response to increasing the number of features usedin the splitting.
define a large number of geometric features and search over a random selection of these for the best split at each node. This latter paper has been influential in my thinking. ... Theorem 1.2 As the number of trees increases, for almost surely all sequences ...
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