Scikit-Explain Documentation
scikit-explain is a user-friendly Python module for machine learning explainability. Current explainability products includes
- Feature importance:
Single- and Multi-pass Permutation Importance (Brieman et al. 2001 , Lakshmanan et al. 2015)
First-order PD/ALE Variance (Greenwell et al. 2018 )
Grouped Permutation Importance (Au et al. 2021)
- Feature Effects/Attributions:
Partial Dependence (PD),
Accumulated local effects (ALE),
Random forest-based feature contributions (treeinterpreter)
Main Effect Complexity (MEC; Molnar et al. 2019)
- Feature Interactions:
Second-order PD/ALE
Interaction Strength and Main Effect Complexity (IAS; Molnar et al. 2019)
Second-order PD/ALE Variance (Greenwell et al. 2018)
Second-order Permutation Importance (Oh et al. 2019)
Friedman H-statistic (Friedman and Popescu 2008)
These explainability methods are discussed at length in Christoph Molnar’s Interpretable Machine Learning. A primary feature of scikit-learn is the accompanying plotting methods, which are desgined to be easy to use while producing publication-level quality figures. Lastly, computations in scikit-explain do leverage parallelization when possible.
The package is under active development and will likely contain bugs or errors. Feel free to raise issues! If you employ scikit-explain in your research, please cite this github and the relevant packages listed above.
Installation
pip install scikit-explain
Documentation
Contribute
Issue Tracker: github.com/monte-flora/scikit-explain/issues
Source Code: github.com/monte-flora/scikit-explain
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.