On Variance Estimation of Random Forests. (arXiv:2202.09008v1 [stat.ML])

Ensemble methods based on subsampling, such as random forests, are popular in
applications due to their high predictive accuracy. Existing literature views a
random forest prediction as an infinite-order incomplete U-statistic to
quantify its uncertainty. However, these methods focus on a small subsampling
size of each tree, which is theoretically valid but practically limited. This
paper develops an unbiased variance estimator based on incomplete U-statistics,
which allows the tree size to be comparable with the overall sample size,
making statistical …

arxiv ml random random forests variance