plot_shap

The plot_shap() function implements a comprehensive set of visualizations for SHAP values, including bar
plots of mean absolute values, directional plots showing positive or negative contribution nature, box plots
illustrating SHAP value distributions by variable, and swarm plots combining individual and distributional
information. This multifaceted approach enables deep understanding of how each feature influences model
predictions.

A minimalistic library specifically designed to make the estimation of Machine
Learning (ML) techniques as easy and accessible as possible, particularly within the framework
of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides
all the essential tools needed to efficiently structure and execute each stage of a predictive
or classification modeling workflow, aligning closely with the fundamental steps of the KDD
methodology, from data selection and preparation, through model building and tuning, to the
interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow is
organized into four core steps; preprocessing(), build_model(), fine_tuning(), and
sensitivity_analysis(). These steps correspond, respectively, to data preparation and
transformation, model construction, hyperparameter optimization, and sensitivity analysis.
The user can access comprehensive model evaluation results including fit assessment metrics,
plots, predictions, and performance diagnostics for ML models implemented through Neural Networks,
Random Forest, XGBoost, and Support Vector Machines algorithms. By streamlining these phases,
'MLwrap' aims to simplify the implementation of ML techniques, allowing analysts and data
scientists to focus on extracting actionable insights and meaningful patterns from large datasets,
in line with the objectives of the KDD process. Inspired by James et al. (2021) "An Introduction
to Statistical Learning: with Applications in R (2nd ed.)" <doi:10.1007/978-1-0716-1418-1> and
Molnar (2025) "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
(3rd ed.)" <https://christophm.github.io/interpretable-ml-book/>.

Javier Martínez García

MLwrap

Machine Learning Modelling for Everyone

Juan José Montaño Moreno

Albert Sesé

plot_shap function

<dl><dt>analysis_object</dt>
<dd>Fitted analysis_object with 'sensitivity_analysis(methods = "SHAP")'.</dd>
<dt>show_table</dt>
<dd>Boolean. Whether to print SHAP summarized results table.</dd></dl>

Arguments

Plotting SHAP Plots — plot_shap

<dl>

<dt>analysis_object</dt>
<dd>Fitted analysis_object with 'sensitivity_analysis(methods = "SHAP")'.</dd>


<dt>show_table</dt>
<dd>Boolean. Whether to print SHAP summarized results table.</dd>

</dl>

plot_shap: Plotting SHAP Plots

Description

Usage

Value

Arguments

Examples