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Metrics Used When Evaluating the Performance of Statistical Classifiers

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  • Daniel R Jeske

    (Department of Statistics, University of California, USA)

Abstract

This article reviews important performance metrics that are used to evaluate the accuracy of statistical classifiers. How the metrics are used to construct Receiver Operator Characteristic (ROC) curves, Predictive ROC (PROC) curves, and Precision-Recall (PR) curves is also discussed. Relationships between the metrics are revealed.

Suggested Citation

  • Daniel R Jeske, 2018. "Metrics Used When Evaluating the Performance of Statistical Classifiers," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(1), pages 7-9, August.
    • Handle: RePEc:adp:jbboaj:v:8:y:2018:i:1:p:7-9
    DOI: 10.19080/BBOAJ.2018.08.555728
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    References listed on IDEAS

    as
    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
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