New Results

Paired evaluation defines performance landscapes for machine learning models

Maulik K. Nariya, Caitlin E. Mills, Peter K. Sorger, Artem Sokolov
doi: https://doi.org/10.1101/2022.09.07.507020

Article Information

doi 
https://doi.org/10.1101/2022.09.07.507020
History 
  • September 12, 2022.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.

Author Information

  1. Maulik K. Nariya1,2,
  2. Caitlin E. Mills2,
  3. Peter K. Sorger1,2 and
  4. Artem Sokolov1,3,*
  1. 1Department of Systems Biology, Harvard Medical School, Boston, MA, USA
  2. 2Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
  3. 3Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
  1. ↵*Corresponding author: artem_sokolov{at}hms.harvard.edu cc lsp-papers{at}hms.harvard.edu
Back to top
Posted September 12, 2022.
Download PDF
Data/Code
Email
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
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area