This release is a pre-release and may not be stable for production use.
A package for calculating ROC curves and Concentrated ROC (CROC) curves written by Dr. S. Joshua Swamidass.
Citation
A CROC Stronger than ROC: Measuring, Visualizing, and Optimizing Early RetrievalS. Joshua Swamidass, Chloe-Agathe Azencott, Kenny Daily and Pierre BaldiBioinformatics, April 2010, doi:10.1093/bioinformatics/btq140
Description
This pure-python package is designed to be a standardized implementation of performance curves and metrics for use either in python scripts or through a simple commandline interface. As a standardized implementation its output is robust enough to be using in publishable scientific work.
With this package, one can easily:
Compute the coordinates of both Accumulation Curves and ROC curves.
Handle ties appropriately using several methods.
Compute the BEDROC metric.
Vertically add and average the performance curves of several cross-validation folds.
Focus on the early part of the ROC curve by using several x-axis transforms.
The docstrings in this module are fairly complete and the scripts provide simple access to the most common functions. Further documentation can be found at http://swami.wustl.edu/CROC/
1 maintainer
Metadata
1 maintainer
Release files for CROC 1.1.Unversioned directory
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| CROC-1.1.Unversioned directory.tar.gz | 13.1 kB | Details |
Release files / CROC-1.1.Unversioned directory.tar.gz
| Download URL | CROC-1.1.Unversioned directory.tar.gz |
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| Size | 13.1 kB |
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