  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.
