Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)
Interpretable Machine Learning and Statistical Inference with Accumula...
ALE data and statistics that describe a trained model
Random variable distributions of ALE statistics for generating p-value...
ALE plots with print and plot methods
subset method for ALEPlots object
summary method for ALEPlots object
Plot method for ALEPlots object
plot method for ModelBoot objects
print Method for ALE object
Print method for ALEPlots object
print method for ModelBoot object
Resolve x_cols and exclude_cols to their standardized format
Retrieve an R object from the first successful source among multiple a...
Statistics and ALE data for a bootstrapped model
Customize plots contained in an ALEPlots object
get method for ALE objects
get method for ALEPlots objects
get method for ModelBoot objects
S7 generic get method for objects in the ale package
Invert ALE Probabilities
plot method for ALE objects
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>.
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