========
Overview
========
Precision-recall-gain curves for Python
* Free software: MIT license
Installation
============
::
pip install precision-recall-gain
You can also install the in-development version with::
pip install https://github.com/crypdick/precision-recall-gain/archive/master.zip
Documentation
=============
https://precision-recall-gain.readthedocs.io/
Development
===========
To run all the tests run::
tox
Note, to combine the coverage data from all the tox environments run:
.. list-table::
:widths: 10 90
:stub-columns: 1
- - Windows
- ::
set PYTEST_ADDOPTS=--cov-append
tox
- - Other
- ::
PYTEST_ADDOPTS=--cov-append tox
References
===========
* [Precision-Recall-Gain Curves: PR Analysis Done Right (2015) by Peter A. Flach and Meelis Kull](https://papers.nips.cc/paper/2015/file/33e8075e9970de0cfea955afd4644bb2-Paper.pdf)
* [sklearn-compatible implementation](https://github.com/scikit-learn/scikit-learn/pull/24121) by [Bradley Fowler](https://github.com/bradleyfowler123)
* [PRG curves](https://www.biostat.wisc.edu/~page/rocprg.pdf) by [David Page](https://www.biostat.wisc.edu/~page/)
* [Blog post by Bradley Fowler](https://snorkel.ai/improving-upon-precision-recall-and-f1-with-gain-metrics/)
* [Original implementation](https://github.com/meeliskull/prg) by [Meelis Kull](https://github.com/meeliskull)
Changelog
=========
0.0.0 (2021-03-20)
------------------
* First release on PyPI.
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