  The relevance of machine learning (ML) in our daily lives is closely
intertwined with its explainability. Explainability can allow end-users to have
a transparent and humane reckoning of a ML scheme's capability and utility. It
will also foster the user's confidence in the automated decisions of a system.
Explaining the variables or features to explain a model's decision is a need of
the present times. We could not really find any work, which explains the
features on the basis of their class-distinguishing abilities (specially when
the real world data are mostly of multi-class nature). In any given dataset, a
feature is not equally good at making distinctions between the different
possible categorizations (or classes) of the data points. In this work, we
explain the features on the basis of their class or category-distinguishing
capabilities. We particularly estimate the class-distinguishing capabilities
(scores) of the variables for pair-wise class combinations. We validate the
explainability given by our scheme empirically on several real-world,
multi-class datasets. We further utilize the class-distinguishing scores in a
latent feature context and propose a novel decision making protocol. Another
novelty of this work lies with a \emph{refuse to render decision} option when
the latent variable (of the test point) has a high class-distinguishing
potential for the likely classes.
