%0 Journal Article
%1 breiman2001random
%A Breiman, Leo
%D 2001
%I Kluwer Academic Publishers
%J Machine Learning
%K classification ensemble forest learning random
%N 1
%P 5-32
%T Random Forests
%U http://dx.doi.org/10.1023/A%3A1010933404324
%V 45
%X Random forests 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 for forests converges a.s. to a limit as the number of trees in the forest becomes large. 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