.. image:: https://zed.uchicago.edu/logo/logo_zedstat.png :height: 150px :align: center
.. image:: https://zenodo.org/badge/529991779.svg :target: https://zenodo.org/badge/latestdoi/529991779
.. class:: no-web no-pdf
:Author: ZeD@UChicago <zed.uchicago.edu> :Description: Tools for ML statistics :Documentation: https://zeroknowledgediscovery.github.io/zedstat/ :Example: https://github.com/zeroknowledgediscovery/zedstat/blob/master/examples/example1.ipynb
Usage:
.. code-block::
from zedstat import zedstat zt=zedstat.processRoc(df=pd.read_csv('roc.csv'), order=3, total_samples=100000, positive_samples=100, alpha=0.01, prevalence=.002)
zt.smooth(STEP=0.001) zt.allmeasures(interpolate=True) zt.usample(precision=3) zt.getBounds()
print(zt.auc())
zt.operating_zone(LRminus=.65) rf0,txt0=zt.interpret(fpr=zt._operating_zone.fpr.values[0],number_of_positives=10) rf1,txt1=zt.interpret(fpr=zt._operating_zone.fpr.values[1],number_of_positives=10) display(zt._operating_zone) print('high precision operation:\n','\n '.join(txt0)) print('high recall operation:\n','\n '.join(txt1))
FAQs
Statistics tools for ML models and deployment
We found that zedstat 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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