  In this paper, error estimates of classification Random Forests are
quantitatively assessed. Based on the initial theoretical framework built by
Bates et al. (2023), the true error rate and expected error rate are
theoretically and empirically investigated in the context of a variety of error
estimation methods common to Random Forests. We show that in the classification
case, Random Forests' estimates of prediction error is closer on average to the
true error rate instead of the average prediction error. This is opposite the
findings of Bates et al. (2023) which are given for logistic regression. We
further show that our result holds across different error estimation strategies
such as cross-validation, bagging, and data splitting.
