print.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>.

Chitu Okoli

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

print.ALE function

<dl><dt>x</dt>
<dd>An object of class <code>ALE</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (currently not used).</dd></dl>

Arguments

print Method for ALE object — print.ALE

<dl>

<dt>x</dt>
<dd>An object of class <code>ALE</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (currently not used).</dd>

</dl>

print.ALE: print Method for ALE object

Description

Value

Arguments

Examples