Calculation of Out-Of-Bag (OOB) error in a random forest (Proc IMSTAT)

[pvareschi]

Re: Predicting Analytics on Big Data

How is the Out-Of-Bag Error calculated for a random forest fitted through RANDOMWOODS in Proc IMSTAT (see page 3-123 of the course text)?

Is it an average of the errors on each out-of-bag sample, calculated as Average Square Error?

[gcjfernandez · Accepted solution]

Yes you are correct. It is the mean of ASE of all the out-of-bag samples.

[lovelmark]

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.