PyMint Documentation

PyMint (Python-based Model INTerpretations) is designed to be a user-friendly package for computing and plotting machine learning interpretation output in Python. Current computation includes partial dependence (PD), accumulated local effects (ALE), random forest-based feature contributions (treeinterpreter), single- and multiple-pass permutation importance, and Shapley Additive Explanations (SHAP). All of these methods are discussed at length in Christoph Molnar’s interpretable ML book (https://christophm.github.io/interpretable-ml-book/). Most calculations can be performed in parallel when multi-core processing is available. The primary feature of this package is the accompanying built-in plotting methods, which are desgined to be easy to use while producing publication-level quality figures.

Installation

pip install py-mint

Documentation

Contribute

• Issue Tracker: github.com/monte-flora/py-mint/issues

• Source Code: github.com/monte-flora/py-mint

Support

If you are having issues, please let us know. We have a mailing list located at: monte.flora@noaa.gov

License

The project is licensed under the BSD license.

Indices and tables

• Index

• Module Index

• Search Page