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 3.8 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      = {0.0.6a4},
  doi          = {10.5281/zenodo.14879819},
  url          = {https://doi.org/10.5281/zenodo.14879819}
}

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.0.6a5)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for artoriusfu from gravatar.com artoriusfu Avatar for lshpaner from gravatar.com lshpaner

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

Classifiers

Report project as malware

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-0.0.6a4.tar.gz (117.2 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-0.0.6a4-py3-none-any.whl (118.8 kB view details)

Uploaded Python 3

File details

Details for the file model_metrics-0.0.6a4.tar.gz.

File metadata

  • Download URL: model_metrics-0.0.6a4.tar.gz
  • Upload date:
  • Size: 117.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.14.7

File hashes

Hashes for model_metrics-0.0.6a4.tar.gz
Algorithm Hash digest
SHA256 1450a920dd00dc35774454193f706864ac09ec674954d951e9a97536291c9e1f
MD5 bcbc9aca983bf0c6f068e0413fc3d972
BLAKE2b-256 8146a8a6bd753b300612d13e4a4f4242a23551f3ee40ad5e955cf3a048d8fa2c

See more details on using hashes here.

File details

Details for the file model_metrics-0.0.6a4-py3-none-any.whl.

File metadata

File hashes

Hashes for model_metrics-0.0.6a4-py3-none-any.whl
Algorithm Hash digest
SHA256 0f8dbb626aac0b2a755d20fe806b028089048a81ac7a35898f24571e11ab65c8
MD5 b589e9da4e5b763eeab35f33c90c498a
BLAKE2b-256 e9764908f46e27adfd09404370f7afea0c9a24a5e05cad662ace3455b70d073c

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 Sentry Error logging StatusPage Status page