Out-of-bag error
Out-of-bag error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating to sub-sample data samples used for training. OOB is the mean prediction error on each training sample, using only the trees that did not have in their bootstrap sample.
Subsampling allows one to define an out-of-bag estimate of the prediction performance improvement by evaluating predictions on those observations which were not used in the building of the next base learner.
