# Functional Correlations in the Pursuit of Performance Assessment of Classifiers

## TL;DR

This paper introduces new correlation measures for evaluating classifiers, demonstrating their effectiveness in analyzing confusion matrices and offering novel tools for performance assessment.

## Contribution

The paper proposes and explores CO-, ANTI-, and COANTI-correlation coefficients as new measures of association for classifier evaluation.

## Key findings

- New correlation coefficients effectively classify confusion matrices.
- The coefficients differ from existing measures in the literature.
- Illustrative examples demonstrate their practical utility.

## Abstract

In statistical classification and machine learning, as well as in social and other sciences, a number of measures of association have been proposed for assessing and comparing individual classifiers, raters, as well as their groups. In this paper, we introduce, justify, and explore several new measures of association, which we call CO-, ANTI- and COANTI-correlation coefficients, that we demonstrate to be powerful tools for classifying confusion matrices. We illustrate the performance of these new coefficients using a number of examples, from which we also conclude that the coefficients are new objects in the sense that they differ from those already in the literature.