  The paper introduces a novel framework for extracting model-agnostic human
interpretable rules to explain a classifier's output. The human interpretable
rule is defined as an axis-aligned hyper-cuboid containing the instance for
which the classification decision has to be explained. The proposed procedure
finds the largest (high \textit{coverage}) axis-aligned hyper-cuboid such that
a high percentage of the instances in the hyper-cuboid have the same class
label as the instance being explained (high \textit{precision}). Novel
approximations to the coverage and precision measures in terms of the
parameters of the hyper-cuboid are defined. They are maximized using
gradient-based optimizers. The quality of the approximations is rigorously
analyzed theoretically and experimentally. Heuristics for simplifying the
generated explanations for achieving better interpretability and a greedy
selection algorithm that combines the local explanations for creating global
explanations for the model covering a large part of the instance space are also
proposed. The framework is model agnostic, can be applied to any arbitrary
classifier, and all types of attributes (including continuous, ordered, and
unordered discrete). The wide-scale applicability of the framework is validated
on a variety of synthetic and real-world datasets from different domains
(tabular, text, and image).
