Accumulated Local Effects (ALE) were initially developed as a modelagnostic approach for global explanations of the results of blackbox 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 bootstrapbased confidence intervals and ALEbased 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. <arXiv:2310.09877>. <doi:10.48550/arXiv.2310.09877>.
Package details 


Author  Chitu Okoli [aut, cre] (<https://orcid.org/0000000155747572>), Dan Apley [cph] (The current code for calculating ALE interaction values is copied with few changes from Dan Apley's ALEPlot package. We gratefully acknowledge his opensource contribution. However, he was not directly involved in the development of this ale package.) 
Maintainer  Chitu Okoli <Chitu.Okoli@skema.edu> 
License  GPL2 
Version  0.3.0 
URL  https://github.com/tripartio/ale https://tripartio.github.io/ale/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.