About Us#

Project Overview#

mlQuantify is an open-source Python library for quantification, providing robust methods to estimate class prevalence with a practical and intuitive API. The library aims to make quantification algorithms and experimental protocols accessible to both researchers and practitioners in data science.

Most Importantly#

mlQuantify was heavily inspired by QuaPy, a leading library for quantification in Python, and by scikit-learn for its simple, familiar interface and workflow design.

By combining key architectural elements, quantification strategies, and evaluation protocols from QuaPy with the ease-of-use and consistent patterns found in scikit-learn, mlQuantify offers a unified, modern experience for quantification tasks.

History#

mlQuantify was started in 2024 by Luiz Fernando Luth Junior and André Gustavo Maletzke, aiming to address the unique challenges and opportunities in quantification. Recognizing that quantification is a niche yet crucial task in real-world data scenarios, mlQuantify fills an essential gap in the Python machine learning ecosystem.

Governance & Contributors#

mlQuantify is built with a community-driven and transparent development process. All major decisions are discussed openly in the project’s issue tracker and forum.

Core Maintainers#

	Luiz Fernando Luth Junior (lead developer, architect & documentation)
	André Gustavo Maletzke (engineering, research & documentation)

Note

For help, bug reports, or feature requests, please use our GitHub issues page rather than emailing contributors directly.

How to Contribute#

Whether you are a researcher, practitioner, or an open source enthusiast, you can contribute by:

Submitting bug reports and feature requests.
Proposing improvements through pull requests.
Helping with documentation.
Sharing quantification use cases and benchmarks.

For more details on contributing, see our contributing guide.

Citing mlQuantify#

If you use mlQuantify in your research or applications, please cite the following:

Bibtex entry:

@software{mlquantify,
  author = {Luiz Fernando Luth Junior and André Gustavo Maletzke},
  title = {mlQuantify: Quantification in Python},
  year = {2024},
  url = {https://github.com/luizfernandolj/mlquantify}
}

Branding & Logo#

High-resolution logos are available in the brand assets folder.

Funding & Support#

mlQuantify is an independent community project. If you’d like to help support its development, consider sponsoring or donating via GitHub Sponsors.

All contributions directly support maintenance and continued innovation in quantification research.