Impact of train/test sample regimen on performance estimate stability of machine learning in cardiov...
Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than...
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Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging
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TN_cdi_doaj_primary_oai_doaj_org_article_3fbe52b623e04aa399b5b0f5b1e1ff34
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3fbe52b623e04aa399b5b0f5b1e1ff34
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-93651-5
