MLcps: machine learning cumulative performance score for classification problems.

Journal: GigaScience
Published Date: Dec 13, 2023

Abstract

BACKGROUND: Assessing the performance of machine learning (ML) models requires careful consideration of the evaluation metrics used. It is often necessary to utilize multiple metrics to gain a comprehensive understanding of a trained model's performance, as each metric focuses on a specific aspect. However, comparing the scores of these individual metrics for each model to determine the best-performing model can be time-consuming and susceptible to subjective user preferences, potentially introducing bias.

Authors

  • Akshay Akshay
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Masoud Abedi
    Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
  • Navid Shekarchizadeh
    Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
  • Fiona C Burkhard
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Mitali Katoch
    Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany.
  • Alex Bigger-Allen
    Biological & Biomedical Sciences Program, Division of Medical Sciences, Harvard Medical School, 02115 Boston, MA, USA.
  • Rosalyn M Adam
    Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA.
  • Katia Monastyrskaya
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Ali Hashemi Gheinani
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.

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