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Contingency

Fast, vectorized metrology with binary contingency counts.

Rapidly calculate binary classifier metrics like MCC, F-Scores, and Average Precision Scores from scalar and binary predictions.

For an overview of features, usage, and performance, see the documentation site. The canonical entry for this publication on the NIST data repository is available at doi:10.18434/mds2-4079.

Installation

contingency can be installed from PyPI:

pip install contingency-tools

If you would like access to the most recent/unstable version on GitHub (before a release):

pip install git+https://github.com/usnistgov/Contingency.git

For access to the NIST PDR entry for contingency, see data.nist.gov.

Contact the PI

Rachael Sexton

  • rachael.sexton@nist.gov
  • NIST Engineering Laboratory
  • Systems Integration Division
  • Information Modeling & Testing Group

Metadata

Release files for contingency-tools 0.2.3

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Source distribution for contingency-tools 0.2.3
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Table of built distributions (wheels) for contingency-tools 0.2.3
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contingency_tools-0.2.3-py3-none-any.whl Python 3 none any Details

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Release files / contingency_tools-0.2.3.tar.gz

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