Random Forests
Paper presentation for CSI5388
PENGCHENG XI
Mar. 23, 2005
Reference
• Leo Breiman, Random Forests,
Machine Learning, 45, 5-32, 2001
Leo Breiman (Professor Emeritus at UCB) is a
member of the National Academy of Sciences
Abstract
• Random forests (RF) are a combination of tree
predictors such that each tree depends on the
values of a random vector sampled
independently and with the same distribution for
all trees in the forest.
• The generalization error of a forest of tree
classifiers depends on the strength of the
individual trees in the forest and the correlation
between them.
• Using a random selection of features to split
each node yields error rates that compare
favorably to Adaboost, and are more robust with
respect to noise.
Introduction
• Improvements in classification accuracy have
resulted from growing an ensemble of trees
and letting them vote for the most popular
class.
• To grow these ensembles, often random
vectors are generated that govern the growth of
each tree in the ensemble.
• Several examples: bagging (Breiman, 1996),
random split selection (Dietterich, 1998), random
subspace (Ho, 1998), written character
recognition (Amit and Geman, 1997)
Introduction (Cont.)
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