Evaluate machine-learning models
Project description
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.
Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
Version 1.2.0 (2023/05/08)
Added: audmetric.linkability()
Changed: speedup audmetric.concordance_cc() and audmetric.pearson_cc() when providing truth and/or prediction as numpy arrays
Version 1.1.6 (2023/01/03)
Fixed: require sphinx-audeering-theme>=1.2.1 to enforce correct theme in published docs
Version 1.1.5 (2023/01/03)
Added: support for Python 3.10
Added: support for Python 3.11
Changed: split API documentation into sub-pages for each function
Version 1.1.4 (2022/07/05)
Fixed: accuracy formula in docstring
Version 1.1.3 (2022/02/16)
Added: reference for CCC formula
Fixed: support pandas series with datatype Int64
Version 1.1.2 (2022/01/11)
Fixed: typo in docstring of audmetric.mean_absolute_error()
Version 1.1.1 (2022/01/03)
Added: Python 3.9 support
Removed: Python 3.6 support
Version 1.1.0 (2021/07/29)
Added: audmetric.utils.infer_labels()
Added: audmetric.equal_error_rate()
Added: audmetric.detection_error_tradeoff()
Version 1.0.1 (2021/06/10)
Fixed: broken package due to missing __init_.py file
Version 1.0.0 (2021/06/09)
Added: initial public release
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