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Ten quick tips for ensuring machine learning model validity

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  • Wilson Wen Bin Goh
  • Mohammad Neamul Kabir
  • Sehwan Yoo
  • Limsoon Wong

Abstract

Author summary: Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives—the user and the developer.

Suggested Citation

  • Wilson Wen Bin Goh & Mohammad Neamul Kabir & Sehwan Yoo & Limsoon Wong, 2024. "Ten quick tips for ensuring machine learning model validity," PLOS Computational Biology, Public Library of Science, vol. 20(9), pages 1-12, September.
    • Handle: RePEc:plo:pcbi00:1012402
    DOI: 10.1371/journal.pcbi.1012402
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    1. Laura J. van 't Veer & Hongyue Dai & Marc J. van de Vijver & Yudong D. He & Augustinus A. M. Hart & Mao Mao & Hans L. Peterse & Karin van der Kooy & Matthew J. Marton & Anke T. Witteveen & George J. S, 2002. "Gene expression profiling predicts clinical outcome of breast cancer," Nature, Nature, vol. 415(6871), pages 530-536, January.
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