|tests| |coverage| |docs| |python-versions| |license|
audmetric includes several equations to estimate the performance of a machine learning prediction algorithm.
Some of the metrics are also available in sklearn_, but we wanted to have a package which depends only on numpy_. For those metrics we included tests that the results are identical to sklearn_.
.. _numpy: https://numpy.org/ .. _sklearn: https://scikit-learn.org/stable/
.. badges images and links: .. |tests| image:: https://github.com/audeering/audmetric/workflows/Test/badge.svg :target: https://github.com/audeering/audmetric/actions?query=workflow%3ATest :alt: Test status .. |coverage| image:: https://codecov.io/gh/audeering/audmetric/branch/main/graph/badge.svg?token=wOMLYzFnDO :target: https://codecov.io/gh/audeering/audmetric/ :alt: code coverage .. |docs| image:: https://img.shields.io/pypi/v/audmetric?label=docs :target: https://audeering.github.io/audmetric/ :alt: audmetric's documentation .. |license| image:: https://img.shields.io/badge/license-MIT-green.svg :target: https://github.com/audeering/audmetric/blob/main/LICENSE :alt: audmetric's MIT license .. |python-versions| image:: https://img.shields.io/pypi/pyversions/audmetric.svg :target: https://pypi.org/project/audmetric/ :alt: audmetric's supported Python versions
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Evaluate machine-learning models
We found that audmetric demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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