Ten quick tips for ensuring machine learning model validity

Wilson Wen Bin Goh, Mohammad Neamul Kabir, Sehwan Yoo and Limsoon Wong

PLOS Computational Biology, 2024, vol. 20, issue 9, 1-12

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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012402 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 12402&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1012402

DOI: 10.1371/journal.pcbi.1012402

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().