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

MDL-motivated compression of GLM ensembles increases interpretability and retains predictive power