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Metrics for Multi-Class Classification: an Overview

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arxiv 2008.05756 v1 pith:BIIRNOPI submitted 2020-08-13 stat.ML cs.LG

Margherita Grandini , Enrico Bagli , Giorgio Visani This is my paper · ORCID
classification stat.MLcs.LG
keywords classificationdifferentmetricsmulti-classdevelopmentlearningmachinemodel
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 66 citations worldwide. Full citation record

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