  We analyze the trade-off between model complexity and accuracy for random
forests by breaking the trees up into individual classification rules and
selecting a subset of them. We show experimentally that already a few rules are
sufficient to achieve an acceptable accuracy close to that of the original
model. Moreover, our results indicate that in many cases, this can lead to
simpler models that clearly outperform the original ones.
