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A package for computing ROC curves and early recognition with CROC.
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swamidasslab/CROC
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================ The CROC Package ================ A package for calculating ROC curves and Concentrated ROC (CROC) curves written by `Dr. S. Joshua Swamidass <http://swami.wustl.edu>`_. Citation -------- | **A CROC Stronger than ROC: Measuring, Visualizing, and Optimizing Early Retrieval** | S. Joshua Swamidass, Chloe-Agathe Azencott, Kenny Daily and Pierre Baldi | *Bioinformatics*, April 2010, `doi:10.1093/bioinformatics/btq140 <http://bioinformatics.oxfordjournals.org/cgi/content/abstract/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.
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A package for computing ROC curves and early recognition with CROC.
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