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      = {0.0.4a9},
  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-0.0.4a9.tar.gz (38.6 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-0.0.4a9-py3-none-any.whl (37.2 kB view details)

Uploaded Python 3

File details

Details for the file model_metrics-0.0.4a9.tar.gz.

File metadata

  • Download URL: model_metrics-0.0.4a9.tar.gz
  • Upload date:
  • Size: 38.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.7

File hashes

Hashes for model_metrics-0.0.4a9.tar.gz
Algorithm Hash digest
SHA256 39a00cd3beb9e6ae84a80db7297b6fed5594545a3584d0fed61a2a4b8e15bc56
MD5 508b989269cbfad1a9a8f68f07e9293b
BLAKE2b-256 1212bbc69096b338565c676daf5629cf3e2321e7e1d5d9cf10e6c26c08b5711b

See more details on using hashes here.

File details

Details for the file model_metrics-0.0.4a9-py3-none-any.whl.

File metadata

File hashes

Hashes for model_metrics-0.0.4a9-py3-none-any.whl
Algorithm Hash digest
SHA256 a718d3bfd55574796b67e07e9caddab9c9798ab7f9662dd847f058989c705cf0
MD5 b3c36dd871e8fe1b55be87d06b90df0f
BLAKE2b-256 07124d218491bc0e6547ce377f6e949cace3180ba5fec094fb8ab04b1daaab19

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