ale - Interpretable Machine Learning and Statistical Inference with
Accumulated Local Effects (ALE)
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 reimplements the
algorithms for calculating ALE data and develops highly
interpretable visualizations for plotting these 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. <doi:10.48550/arXiv.2310.09877>.
