This release is a pre-release and may not be stable for production use.
Welcome to Model Metrics! Model Metrics is a versatile Python library designed to streamline the evaluation and interpretation of machine learning models. It provides a robust framework for generating predictions, computing model metrics, analyzing feature importance, and visualizing results. Whether you're working with SHAP values, model coefficients, confusion matrices, ROC curves, precision-recall plots, and other key performance indicators.
Prerequisites
Before you install model_metrics, ensure your system meets the following requirements:
Python: Version3.7.4or higher.
Additionally, model_metrics depends on the following packages, which will be automatically installed when you install model_metrics:
matplotlib: version3.5.3or higher, but capped at3.9.2numpy: version1.21.6or higher, but capped at2.1.0plotly: version5.18.0or higher, but capped at5.24.0scikit-learn: version1.0.2or higher, but capped at1.5.2seaborn: version0.12.2or higher, but capped below0.13.2shap: version0.41.0or higher, but capped below0.46.0tqdm: version4.66.4or higher, but capped below4.67.1
💾 Installation
To install model_metrics, simply run the following command in your terminal:
pip install model_metrics
📄 Official Documentation
https://lshpaner.github.io/model_metrics
🌐 Authors' Website
🙏 Acknowledgements
Gratitude goes to Dr. Ebrahim Tarshizi for his mentorship during the University of San Diego M.S. Applied Data Science Program, as well as the Shiley-Marcos School of Engineering for its support.
Special thanks to Dr. Alex Bui for his invaluable guidance and support, and to Panayiotis Petousis and Arthur Funell for their exceptional teamwork in maintaining a solid data science foundation at UCLA CTSI. Special thanks to those who have contributed to the development of this library, provided guidance, and supported a strong data science foundation. This work is built upon a collaborative effort that values knowledge sharing, teamwork, and dedication to advancing model evaluation practices.
2 maintainers
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