  We show that the byproducts of the standard training process of a random
forest yield not only the well known and almost computationally free out-of-bag
point estimate of the model generalization error, but also give a direct path
to compute confidence intervals for the generalization error which avoids
processes of data splitting and model retraining. Besides the low computational
cost involved in their construction, these confidence intervals are shown
through simulations to have good coverage and appropriate shrinking rate of
their width in terms of the training sample size.
