  In machine learning algorithm design, there exists a trade-off between the
interpretability and performance of the algorithm. In general, algorithms which
are simpler and easier for humans to comprehend tend to show worse performance
than more complex, less transparent algorithms. For example, a random forest
classifier is likely to be more accurate than a simple decision tree, but at
the expense of interpretability. In this paper, we present an ensemble
classifier design which classifies easier inputs using a highly-interpretable
classifier (i.e., white box model), and more difficult inputs using a more
powerful, but less interpretable classifier (i.e., black box model).
