  Over the years, ensemble methods have become a staple of machine learning.
Similarly, generalized linear models (GLMs) have become very popular for a wide
variety of statistical inference tasks. The former have been shown to enhance
out- of-sample predictive power and the latter possess easy interpretability.
Recently, ensembles of GLMs have been proposed as a possibility. On the
downside, this approach loses the interpretability that GLMs possess. We show
that minimum description length (MDL)-motivated compression of the inferred
ensembles can be used to recover interpretability without much, if any,
downside to performance and illustrate on a number of standard classification
data sets.
