search for: randomforest2001

...nd not pruned back. 5. Use the tree to predict out-of-bag data. 6. In the end, use the predictions on out-of-bag data to form majority votes. 7. Prediction of test data is done by majority votes from predictions from the ensemble of trees. In the tech report http://oz.berkeley.edu/users/breiman/randomforest2001.pdf, Breiman showed that this technique is very competitive to boosting classification trees. In our own experience, it is competitive with nonlinear classifiers such as artificial neural nets and support vector machines. Two of the significant advantages of random forests over other methods (IMHO...

...nd not pruned back. 5. Use the tree to predict out-of-bag data. 6. In the end, use the predictions on out-of-bag data to form majority votes. 7. Prediction of test data is done by majority votes from predictions from the ensemble of trees. In the tech report http://oz.berkeley.edu/users/breiman/randomforest2001.pdf, Breiman showed that this technique is very competitive to boosting classification trees. In our own experience, it is competitive with nonlinear classifiers such as artificial neural nets and support vector machines. Two of the significant advantages of random forests over other methods (IMHO...