Skip to main content
Pre-release

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 3.7.4 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 at 3.10.0
  • numpy: version 1.21.6 or higher, but capped at 2.1.0
  • pandas: version 1.3.5 or higher, but capped at 2.2.3
  • plotly: version 5.18.0 or higher, but capped at 5.24.1
  • scikit-learn: version 1.0.2 or higher, but capped at 1.5.2
  • scipy: version 1.8 or higher, but capped at 1.14.0
  • shap: version 0.41.0 or higher, but capped below 0.46.0
  • statsmodels: version 0.12.2 or higher, but capped below 0.14.4
  • tqdm: version 4.66.4 or higher, but capped below 4.67.1

💾 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_docs

🌐 Author's Website

  1. Leonid 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},
  title        = {Model Metrics},
  month        = feb,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {0.0.4a6},
  doi          = {10.5281/zenodo.14879819},
  url          = {https://doi.org/10.5281/zenodo.14879819}
}

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

model_metrics-0.0.4a6.tar.gz (36.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

model_metrics-0.0.4a6-py3-none-any.whl (35.2 kB view details)

Uploaded Python 3

File details

Details for the file model_metrics-0.0.4a6.tar.gz.

File metadata

  • Download URL: model_metrics-0.0.4a6.tar.gz
  • Upload date:
  • Size: 36.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for model_metrics-0.0.4a6.tar.gz
Algorithm Hash digest
SHA256 1830da04c4bead9769620d2643d56875312596bf2b6785535d84f08e4bf84e15
MD5 785141653a280518d110e140aa9e6fe8
BLAKE2b-256 4273034f962929dd9565d26d67206157125838376825935b20d901969f844be8

See more details on using hashes here.

File details

Details for the file model_metrics-0.0.4a6-py3-none-any.whl.

File metadata

File hashes

Hashes for model_metrics-0.0.4a6-py3-none-any.whl
Algorithm Hash digest
SHA256 9b126e9c9e4256a272cc895fe97a1cc81109a2d8754a4ceea028ba6bc408751d
MD5 7e6bf567084d4142bc490f8e9d7404b9
BLAKE2b-256 a2fdcd71e9947c9cc7eae5b695faf19ac4c1f4cf53e836f067f03964ab72fbc7

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page