Precision-recall-gain curves for Python
Project description
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
Development
To run all the tests run:
tox
Note, to combine the coverage data from all the tox environments run:
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Hashes for precision-recall-gain-0.1.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3431e02edcbe7b2254b53ab4447e42e409dea8060efaf259e8378eb9e23816d1 |
|
MD5 | 90fc464cc9986c09520f5508192325fb |
|
BLAKE2b-256 | e717f06820fcafe93299d1277ce6ff52139b54056cf2f8932b7c5613a5fbbdaf |