{"repo":"nubank/fklearn","free":true,"listed":false,"github":"https://github.com/nubank/fklearn","clone":"git clone https://github.com/nubank/fklearn.git","description":"fklearn: Functional Machine Learning","language":"Jupyter Notebook","stars":1547,"topics":["data-science","machine-learning","python","data-analysis","ml"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"fklearn: Functional Machine Learning fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning. The name is a reference to the widely known scikit-learn library. fklearn Principles 1. Validation should reflect real-life situations. 2. Production models should match validated models. 3. Models should be production-ready with few extra steps. 4. Reproducibility and in-depth analysis of model results should be easy to achieve. Documentation Getting Started API Docs Contributing Installation To install via pip: To install with optional dependencies: Development with UV fklearn uses uv for dependency management. uv sync creates a virtual environment, installs all locked dependencies, and installs fklearn itself in editable mode (the default for uv projects) so changes under src/ are picked up without reinstalling. Setup The dev dependency group (pytest, ruff, mypy, hypothesis) is included by default via tool.uv.default-groups , so uv sync alone is enough for most development workflows. Running Tests Linting Adding Dependencies Note for Nubank contributors Regenerate the lockfile with --default-index https://pypi.org/simple/ : License Apache License 2.0","default_branch":null,"files":null,"tree":[],"storefront":"/r/nubank","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nubank/fklearn/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}