Scikit-Explain Documentation — Scikit-Explain latest documentation (original) (raw)

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)
  • SHAP
  • 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)
  • SHAP
  • 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

• ExplainToolkit
  • ExplainToolkit
• Accumulated Local Effects
  • General Use
  • Comparing Methods
• Partial Dependence
  • General Use
  • Color-coding ICE Curves
  • Comparing Methods
• Feature Attributions
  • Feature Contributions Documentation
• SHAP-Style
  • Additional Feature Attribution Plots Documentation
• Permutation Importance
  • General Use
  • Comparing Methods

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.