Metadata-Version: 2.1
Name: CompStats
Version: 0.1.0
Summary: CompStats implements an evaluation methodology for statistically analyzing competition results and competition
Project-URL: Homepage, https://compstats.readthedocs.io
Project-URL: Repository, https://github.com/INGEOTEC/CompStats
Project-URL: Issues, https://github.com/INGEOTEC/CompStats/issues
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scikit-learn>=1.3.0
Requires-Dist: pandas
Requires-Dist: seaborn>=0.13.0

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CompStats
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Collaborative competitions have gained popularity in the scientific and technological fields. These competitions involve defining tasks, selecting evaluation scores, and devising result verification methods. In the standard scenario, participants receive a training set and are expected to provide a solution for a held-out dataset kept by organizers. An essential challenge for organizers arises when comparing algorithms' performance, assessing multiple participants, and ranking them. Statistical tools are often used for this purpose; however, traditional statistical methods often fail to capture decisive differences between systems' performance. CompStats implements an evaluation methodology for statistically analyzing competition results and competition. CompStats offers several advantages, including off-the-shell comparisons with correction mechanisms and the inclusion of confidence intervals. 
