Prediction Error Estimation in Random Forests

Ian Krupkin; Johanna Hardin

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arXiv · 2309.00736

Prediction Error Estimation in Random Forests

Ian Krupkin·Johanna Hardin

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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 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.

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Ian Krupkin, Johanna Hardin. 2023-09-01. Prediction Error Estimation in Random Forests. https://arxiv.org/abs/2309.00736

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