IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i3d10.1007_s00180-024-01539-5.html
   My bibliography  Save this article

Performance of evaluation metrics for classification in imbalanced data

Author

Listed:
  • Alex Cruz Huayanay

    (Pontificia Universidad Católica del Perú
    USP/UFSCar)

  • Jorge L. Bazán

    (University of São Paulo)

  • Cibele M. Russo

    (University of São Paulo)

Abstract

This paper investigates the effectiveness of various metrics for selecting the adequate model for binary classification when data is imbalanced. Through an extensive simulation study involving 12 commonly used metrics of classification, our findings indicate that the Matthews Correlation Coefficient, G-Mean, and Cohen’s kappa consistently yield favorable performance. Conversely, the area under the curve and Accuracy metrics demonstrate poor performance across all studied scenarios, while other seven metrics exhibit varying degrees of effectiveness in specific scenarios. Furthermore, we discuss a practical application in the financial area, which confirms the robust performance of these metrics in facilitating model selection among alternative link functions.

Suggested Citation

  • Alex Cruz Huayanay & Jorge L. Bazán & Cibele M. Russo, 2025. "Performance of evaluation metrics for classification in imbalanced data," Computational Statistics, Springer, vol. 40(3), pages 1447-1473, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01539-5
    DOI: 10.1007/s00180-024-01539-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01539-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01539-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01539-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.