  Recently, a method [7] was proposed to generate contrastive explanations for
differentiable models such as deep neural networks, where one has complete
access to the model. In this work, we propose a method, Model Agnostic
Contrastive Explanations Method (MACEM), to generate contrastive explanations
for \emph{any} classification model where one is able to \emph{only} query the
class probabilities for a desired input. This allows us to generate contrastive
explanations for not only neural networks, but models such as random forests,
boosted trees and even arbitrary ensembles that are still amongst the
state-of-the-art when learning on structured data [13]. Moreover, to obtain
meaningful explanations we propose a principled approach to handle real and
categorical features leading to novel formulations for computing pertinent
positives and negatives that form the essence of a contrastive explanation. A
detailed treatment of the different data types of this nature was not performed
in the previous work, which assumed all features to be positive real valued
with zero being indicative of the least interesting value. We part with this
strong implicit assumption and generalize these methods so as to be applicable
across a much wider range of problem settings. We quantitatively and
qualitatively validate our approach over 5 public datasets covering diverse
domains.
