Metrics for Multi-Class Classification: an Overview

TL;DR

This paper reviews various metrics for evaluating multi-class classification models, discussing their advantages, disadvantages, and applications during model development to aid in performance assessment.

Contribution

It provides a comprehensive overview of key multi-class metrics, highlighting their specific uses and limitations in the model development process.

Findings

Identifies the most promising multi-class metrics.

Analyzes advantages and disadvantages of each metric.

Suggests appropriate metrics for different development stages.

Abstract

Classification tasks in machine learning involving more than two classes are known by the name of "multi-class classification". Performance indicators are very useful when the aim is to evaluate and compare different classification models or machine learning techniques. Many metrics come in handy to test the ability of a multi-class classifier. Those metrics turn out to be useful at different stage of the development process, e.g. comparing the performance of two different models or analysing the behaviour of the same model by tuning different parameters. In this white paper we review a list of the most promising multi-class metrics, we highlight their advantages and disadvantages and show their possible usages during the development of a classification model.