# Functional Correlations in the Pursuit of Performance Assessment of Classifiers

arXiv: https://arxiv.org/abs/1905.04667 · HTML: https://mlsanity.com/paper/1905.04667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 1 of 1)

- [Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints](https://mlsanity.com/paper/2506.21623/) (2025)

## Abstract (arXiv)

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.