  We propose a fast, model agnostic method for finding interpretable
counterfactual explanations of classifier predictions by using class
prototypes. We show that class prototypes, obtained using either an encoder or
through class specific k-d trees, significantly speed up the the search for
counterfactual instances and result in more interpretable explanations. We
introduce two novel metrics to quantitatively evaluate local interpretability
at the instance level. We use these metrics to illustrate the effectiveness of
our method on an image and tabular dataset, respectively MNIST and Breast
Cancer Wisconsin (Diagnostic). The method also eliminates the computational
bottleneck that arises because of numerical gradient evaluation for
$\textit{black box}$ models.
