Out-of-Bag (OOB) is particularly useful when you want to resample data from very large datasets, as it can be a much more computationally-friendly alternative to cross-validation.

University of Michigan - Ann Arbor

Out-of-Bag (OOB) error estimation is a technique used to evaluate the test error of a bagged model without requiring cross-validation. An observation is considered 'out-of-bag' if it was not used to fit a given bagged tree. On average, each observation is OOB for approximately $$B/3$$ of the $$B$$ total trees. To predict the response for the $$i$$th observation, the model uses only the subset of trees where that observation was OOB. For regression trees, these specific OOB predictions are averaged to yield a single prediction for the $$i$$th observation, whereas for classification trees, a majority vote is taken. Finally, these predictions are compared against the true responses to calculate the overall OOB error, such as the Mean Squared Error (MSE) for regression or the classification error rate.

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