Paired evaluation defines performance landscapes for machine learning models

Maulik K. Nariya; Caitlin E. Mills; Peter K. Sorger; Artem Sokolov

doi:10.1101/2022.09.07.507020

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Paired evaluation defines performance landscapes for machine learning models

View ORCID ProfileMaulik K. Nariya, View ORCID ProfileCaitlin E. Mills, View ORCID ProfilePeter K. Sorger, View ORCID ProfileArtem Sokolov

doi: https://doi.org/10.1101/2022.09.07.507020

Maulik K. Nariya

1Department of Systems Biology, Harvard Medical School, Boston, MA, USA

2Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA

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Caitlin E. Mills

2Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA

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Peter K. Sorger

1Department of Systems Biology, Harvard Medical School, Boston, MA, USA

2Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA

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Artem Sokolov

1Department of Systems Biology, Harvard Medical School, Boston, MA, USA

3Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

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For correspondence: [email protected] [email protected]

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Posted September 12, 2022.

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Paired evaluation defines performance landscapes for machine learning models

Maulik K. Nariya, Caitlin E. Mills, Peter K. Sorger, Artem Sokolov

bioRxiv 2022.09.07.507020; doi: https://doi.org/10.1101/2022.09.07.507020

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Paired evaluation defines performance landscapes for machine learning models

Maulik K. Nariya, Caitlin E. Mills, Peter K. Sorger, Artem Sokolov

bioRxiv 2022.09.07.507020; doi: https://doi.org/10.1101/2022.09.07.507020

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