I do not think you can start with anything precise. You have to achieve such precision as you can, as you go along. —Bertrand Russell
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Change history
16 September 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41592-021-01289-y
References
Lever, J., Krzywinski, M. & Altman, N. Nat. Methods 13, 603–604 (2016).
Krzywinski, M. & Altman, N. Nat. Methods 11, 215–216 (2014).
Lever, J., Krzywinski, M. & Altman, N. Nat. Methods 13, 541–542 (2016).
Altman, N. & Krzywinski, M. Nat. Methods 18, 224–225 (2021).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Altman, N., Krzywinski, M. Graphical assessment of tests and classifiers. Nat Methods 18, 840–842 (2021). https://doi.org/10.1038/s41592-021-01232-1
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41592-021-01232-1
This article is cited by
-
A primer on the use of machine learning to distil knowledge from data in biological psychiatry
Molecular Psychiatry (2024)
-
Convolutional neural networks
Nature Methods (2023)
-
The class imbalance problem
Nature Methods (2021)