plot_shap

The plot_shap() function implements comprehensive SHAP (SHapley Additive
exPlanations) value visualizations where SHAP values represent each
feature's marginal contribution to model output based on cooperative game
theory principles. Provides four visualization modalities: bar plots of mean
absolute SHAP values ranking features by average impact magnitude,
directional plots showing feature-value correlation with SHAP magnitude and
sign, box plots illustrating SHAP value distributions across instances, and
swarm plots combining individual prediction contributions with
distributional information.

A minimal 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
essential tools to 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(). It also includes global and pairwise interaction analysis based on
Friedman’s H-statistic to support a more detailed interpretation of complex feature
relationships.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' (Extreme Gradient Boosting), and 'Support Vector
Machines' (SVM) 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.

Albert Sesé

MLwrap

Machine Learning Modelling for Everyone

Javier Martínez García

Juan José Montaño Moreno

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

See Also

Examples