Computer Science > Machine Learning
[Submitted on 16 Jul 2012]
Title:Accuracy Measures for the Comparison of Classifiers
View PDFAbstract:The selection of the best classification algorithm for a given dataset is a very widespread problem. It is also a complex one, in the sense it requires to make several important methodological choices. Among them, in this work we focus on the measure used to assess the classification performance and rank the algorithms. We present the most popular measures and discuss their properties. Despite the numerous measures proposed over the years, many of them turn out to be equivalent in this specific case, to have interpretation problems, or to be unsuitable for our purpose. Consequently, classic overall success rate or marginal rates should be preferred for this specific task.
Submission history
From: Hocine Cherifi [view email] [via CCSD proxy][v1] Mon, 16 Jul 2012 08:49:34 UTC (233 KB)
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