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This release is a pre-release and may not be stable for production use.


PyPI Downloads License: MIT

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: Version 3.8 or higher.

Additionally, model_metrics depends on the following packages, which will be automatically installed when you install model_metrics:

  • matplotlib: version 3.5.3 or higher, but capped below 3.11
  • matplotlib-venn: version 1.0.0 or higher, but capped at 1.1.2
  • numpy: version 1.21.6 or higher, but capped below 2.2
  • pandas: version 1.3.5 or higher, but capped below 2.3
  • plotly: version 5.18.0 or higher, but capped below 5.25
  • scikit-learn: version 1.0.2 or higher
  • scipy: version 1.7.3 or higher
  • statsmodels: version 0.13 or higher, but capped below 0.15
  • shap: version 0.41.0 or higher, but capped below 0.52
  • tqdm: version 4.66.4 or higher

💾 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_docs

🌐 Authors' Website(s)

  1. Leon Shpaner

🙏 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, and to Panayiotis Petousis, PhD, and Arthur Funnell for their invaluable guidance and their exceptional teamwork in maintaining a strong data science infrastructure at UCLA CTSI. Their leadership and support have helped foster the kind of collaborative environment that makes work like this possible. Additional thanks to all who offered guidance and encouragement throughout the development of this library. This project reflects a shared commitment to knowledge sharing, teamwork, and advancing model evaluation practices.

⚖️ License

model_metrics is distributed under the MIT License. See LICENSE for more information.

⚓ Support

If you have any questions or issues with model_metrics, please open an issue on this GitHub repository.

📚 Citing model_metrics

If you use model_metrics in your research or projects, please consider citing it.

@software{shpaner_2025_14879819,
  author       = {Shpaner, Leonid and Funnell, Arthur},
  title        = {Model Metrics},
  month        = feb,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {0.0.6a3},
  doi          = {10.5281/zenodo.14879819},
  url          = {https://doi.org/10.5281/zenodo.14879819}
}

Project links

Key dates

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  • Released:
Newer release available (0.0.6a5)

2 maintainers

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Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Leonid Shpaner, Arthur Funnell

License

MIT License

Requires

Python >=3.8

Tags

model-evaluation machine-learning metrics visualization classification regression roc-curve precision-recall calibration confusion-matrix feature-importance feature-selection threshold-tuning model-performance scikit-learn

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