Reconnoitering the class distinguishing abilities of the features, to know them better

TL;DR

This paper introduces a method to explain features based on their ability to distinguish between classes in multi-class datasets, enhancing interpretability and decision-making in machine learning models.

Contribution

It proposes a novel scheme to quantify feature class-distinguishing capabilities and applies it to improve explainability and decision protocols in ML models.

Findings

Empirically validated on multiple real-world datasets.

Introduced a decision-making protocol utilizing class-distinguishing scores.

Implemented a 'refuse to render decision' option based on feature scores.

Abstract

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.