  For a voting ensemble that selects an odd-sized subset of the ensemble
classifiers at random for each example, applies them to the example, and
returns the majority vote, we show that any number of voters may minimize the
error rate over an out-of-sample distribution. The optimal number of voters
depends on the out-of-sample distribution of the number of classifiers in
error. To select a number of voters to use, estimating that distribution then
inferring error rates for numbers of voters gives lower-variance estimates than
directly estimating those error rates.
