Title: Learning Qualitatively Diverse and Interpretable Rules for Classification,
Abstract: There has been growing interest in developing accurate models that can also
be explained to humans. Unfortunately, if there exist multiple distinct but
accurate models for some dataset, current machine learning methods are unlikely
to find them: standard techniques will likely recover a complex model that
combines them. In this work, we introduce a way to identify a maximal set of
distinct but accurate models for a dataset. We demonstrate empirically that, in
situations where the data supports multiple accurate classifiers, we tend to
recover simpler, more interpretable classifiers rather than more complex ones.