%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 %R 10.1023/A:1010933404324 %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

@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 }, added-at = {2015-03-19T20:35:07.000+0100}, author = {Breiman, Leo}, biburl = {https://www.bibsonomy.org/bibtex/2b8187107bf870043f2f93669958858f1/sdo}, description = {Random Forests - Springer}, doi = {10.1023/A:1010933404324}, interhash = {4450d2e56555e7cb8f3817578e1dd4da}, intrahash = {b8187107bf870043f2f93669958858f1}, issn = {0885-6125}, journal = {Machine Learning}, keywords = {classification ensemble forest learning random}, language = {English}, number = 1, pages = {5-32}, publisher = {Kluwer Academic Publishers}, timestamp = {2015-03-19T20:35:07.000+0100}, title = {Random Forests}, url = {http://dx.doi.org/10.1023/A%3A1010933404324}, volume = 45, year = 2001 }