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Computer Science > Machine Learning

arXiv:2010.16061 (cs)
[Submitted on 11 Oct 2020]

Title:Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Authors:David M. W. Powers
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Abstract:Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform better under any of these commonly used measures. We discuss several concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case to the general multi-class case.
Comments: 27 pages, 7 figures. Updated and fixed egregious formatting errors (including a table overlapping text) that were introduced by the publisher. This open access journal appears to have been discontinued. arXiv admin note: text overlap with arXiv:1504.00854
Subjects: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
Cite as: arXiv:2010.16061 [cs.LG]
  (or arXiv:2010.16061v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2010.16061
Journal reference: International Journal of Machine Learning Technology 2:1 (2011), pp.37-63

Submission history

From: David Powers [view email]
[v1] Sun, 11 Oct 2020 02:15:11 UTC (1,632 KB)
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