IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0092209.html
   My bibliography  Save this article

Area under Precision-Recall Curves for Weighted and Unweighted Data

Author

Listed:
  • Jens Keilwagen
  • Ivo Grosse
  • Jan Grau

Abstract

Precision-recall curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers. However, for many applications class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to data points. Computing the area under the precision-recall curve requires interpolating between adjacent supporting points, but previous interpolation schemes are not directly applicable to weighted data. Hence, even in cases where weights were available, they had to be neglected for assessing classifiers using precision-recall curves. Here, we propose an interpolation for precision-recall curves that can also be used for weighted data, and we derive conditions for classification scores yielding the maximum and minimum area under the precision-recall curve. We investigate accordances and differences of the proposed interpolation and previous ones, and we demonstrate that taking into account existing weights of test data is important for the comparison of classifiers.

Suggested Citation

  • Jens Keilwagen & Ivo Grosse & Jan Grau, 2014. "Area under Precision-Recall Curves for Weighted and Unweighted Data," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
  • Handle: RePEc:plo:pone00:0092209
    DOI: 10.1371/journal.pone.0092209
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092209
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0092209&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0092209?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. W. Frank Lenoir & Micaela Morgado & Peter C. DeWeirdt & Megan McLaughlin & Audrey L. Griffith & Annabel K. Sangree & Marissa N. Feeley & Nazanin Esmaeili Anvar & Eiru Kim & Lori L. Bertolet & Medina C, 2021. "Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Wahid-Ul-Ashraf, Akanda & Budka, Marcin & Musial, Katarzyna, 2019. "How to predict social relationships — Physics-inspired approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1110-1129.
    3. 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.
    4. Giambattista Albora & Matteo Straccamore & Andrea Zaccaria, 2024. "Machine learning-based similarity measure to forecast M&A from patent data," Papers 2404.07179, arXiv.org.
    5. Stan Hatko, 2017. "The Bank of Canada 2015 Retailer Survey on the Cost of Payment Methods: Nonresponse," Technical Reports 107, Bank of Canada.
    6. T. Villmann & M. Kaden & W. Hermann & M. Biehl, 2018. "Learning vector quantization classifiers for ROC-optimization," Computational Statistics, Springer, vol. 33(3), pages 1173-1194, September.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0092209. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.