Confidence Intervals for the Generalisation Error of Random Forests
Methodology
2022-01-28 v1
Abstract
Out-of-bag error is commonly used as an estimate of generalisation error in ensemble-based learning models such as random forests. We present confidence intervals for this quantity using the delta-method-after-bootstrap and the jackknife-after-bootstrap techniques. These methods do not require growing any additional trees. We show that these new confidence intervals have improved coverage properties over the naive confidence interval, in real and simulated examples.
Keywords
Cite
@article{arxiv.2201.11210,
title = {Confidence Intervals for the Generalisation Error of Random Forests},
author = {Samyak Rajanala and Stephen Bates and Trevor Hastie and Robert Tibshirani},
journal= {arXiv preprint arXiv:2201.11210},
year = {2022}
}
Comments
25 pages, 8 tables, 8 figures