@article{breiman2001random, abstract = {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 }, author = {Breiman, Leo}, doi = {10.1023/A:1010933404324}, interhash = {4450d2e56555e7cb8f3817578e1dd4da}, intrahash = {b8187107bf870043f2f93669958858f1}, issn = {0885-6125}, journal = {Machine Learning}, language = {English}, number = 1, pages = {5-32}, publisher = {Kluwer Academic Publishers}, title = {Random Forests}, url = {http://dx.doi.org/10.1023/A%3A1010933404324}, volume = 45, year = 2001 }