Better insights into Machine Learning models performance
Project description
Modelsight
Better insights into Machine Learning models performance.
Modelsight is a collection of functions that create publication-ready graphics for the visual evaluation of binary classifiers adhering to the scikit-learn interface.
Modelsight is strongly oriented towards the evaluation of already fitted ExplainableBoostingClassifier
of the interpretml package.
Installation
$ pip install modelsight
Usage
See the example notebook.
Contributing
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
License
modelsight
was created by Francesco Pisu. It is licensed under the terms of the GNU General Public License v3.0 license.
Roadmap
Features:
- Average Receiver-operating characteristic curves
- Average Precision-recall curves
- Feature importance (Global explanation)
- Individualized explanations (Local explanation)
CI/CD:
- Integration with GH Actions
Credits
modelsight
was created with cookiecutter
and the py-pkgs-cookiecutter
template.
Project details
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