Out-of-bag error while using Random forest classifier


I am writing a paper based on a classification that I carried out in SNAP using the Random Forest classifier.
I need to indicate what the Out of Bag (OOB) error was like. It would also help if there was some information on the Mtry parameter.

Looking forward to some help.


Hi Pamela,

I am currently trying to do the same in Snap.
Did you ever manage it throught SNAP, i.e. indicate the OOB error?


did you get the information?

Out of bag error is simply error computed on samples not seen during training. Out-of-bag estimate for the generalization error is the error rate of the out-of-bag classifier on the training set (compare it with known yi’s). In Breiman’s original implementation of the random forest algorithm, each tree is trained on about 2/3 of the total training data. As the forest is built, each tree can thus be tested (similar to leave one out cross validation) on the samples not used in building that tree. This is the out of bag error estimate - an internal error estimate of a random forest as it is being constructed.