Prediction Error Estimation in Random Forests

arXiv:2309.00736v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv classification context cs.lg error forests framework paper prediction random random forests rate stat.ml true type