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When to consult precision-recall curves

Author

Listed:
  • Jonathan Cook

    (Public Company Accounting Oversight Board)

  • Vikram Ramadas

    (Public Company Accounting Oversight Board)

Abstract

Receiver operating characteristic (ROC) curves are commonly used to evaluate predictions of binary outcomes. When there is a small percentage of items of interest (as would be the case with fraud detection, for example), ROC curves can provide an inflated view of performance. This can cause challenges in determining which set of predictions is better. In this article, we discuss the condi- tions under which precision-recall curves may be preferable to ROC curves. As an illustrative example, we compare two commonly used fraud predictors (Beneish’s [1999, Financial Analysts Journal 55: 24–36] M score and Dechow et al.’s [2011, Contemporary Accounting Research 28: 17–82] F score) using both ROC and precision-recall curves. To aid the reader with using precision-recall curves, we also introduce the command prcurve to plot them.

Suggested Citation

  • Jonathan Cook & Vikram Ramadas, 2020. "When to consult precision-recall curves," Stata Journal, StataCorp LP, vol. 20(1), pages 131-148, March.
    • Handle: RePEc:tsj:stataj:v:20:y:2020:i:1:p:131-148
    DOI: 10.1177/1536867X20909693
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-1/st0591/
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    Cited by:

    1. Esmeli, Ramazan & Bader-El-Den, Mohamed & Abdullahi, Hassana, 2022. "An analyses of the effect of using contextual and loyalty features on early purchase prediction of shoppers in e-commerce domain," Journal of Business Research, Elsevier, vol. 147(C), pages 420-434.
    2. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    3. Codruț-Georgian Artene & Ciprian Oprișa & Cristian Nicolae Buțincu & Florin Leon, 2023. "Finding Patient Zero and Tracking Narrative Changes in the Context of Online Disinformation Using Semantic Similarity Analysis," Mathematics, MDPI, vol. 11(9), pages 1-26, April.

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