  Increasing number of sectors which affect human lives, are using Machine
Learning (ML) tools. Hence the need for understanding their working mechanism
and evaluating their fairness in decision-making, are becoming paramount,
ushering in the era of Explainable AI (XAI). In this contribution we introduced
a few intrinsically interpretable models which are also capable of dealing with
missing values, in addition to extracting knowledge from the dataset and about
the problem. These models are also capable of visualisation of the classifier
and decision boundaries: they are the angle based variants of Learning Vector
Quantization. We have demonstrated the algorithms on a synthetic dataset and a
real-world one (heart disease dataset from the UCI repository). The newly
developed classifiers helped in investigating the complexities of the UCI
dataset as a multiclass problem. The performance of the developed classifiers
were comparable to those reported in literature for this dataset, with
additional value of interpretability, when the dataset was treated as a binary
class problem.
