  With the advent of highly predictive but opaque deep learning models, it has
become more important than ever to understand and explain the predictions of
such models. Existing approaches define interpretability as the inverse of
complexity and achieve interpretability at the cost of accuracy. This
introduces a risk of producing interpretable but misleading explanations. As
humans, we are prone to engage in this kind of behavior \cite{mythos}. In this
paper, we take a step in the direction of tackling the problem of
interpretability without compromising the model accuracy. We propose to build a
Treeview representation of the complex model via hierarchical partitioning of
the feature space, which reveals the iterative rejection of unlikely class
labels until the correct association is predicted.
