ale-package: Interpretable Machine Learning and Statistical Inference with...

In ale: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)

Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)

Description

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package rewrites the original code from the {ALEPlot} package for calculating ALE data and it completely reimplements the plotting of ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. https://arxiv.org/abs/2310.09877.

Author(s)

Chitu Okoli [e-mail]

References

Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. https://arxiv.org/abs/2310.09877.

See Also

Useful links:

• https://github.com/tripartio/ale

• https://tripartio.github.io/ale/

• Report bugs at https://github.com/tripartio/ale/issues

ale documentation built on May 29, 2024, 10:33 a.m.