semvec/pypi-packages huggingface dataset:
precision-recall-gain 0.1.1
Precision-recall-gain curves for Python
License: MIT
Requires Python: >=3.9

========
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.