table_shap_results

The table_shap_results() function processes previously calculated SHAP
(SHapley Additive exPlanations) values and generates summarized metrics
including mean absolute value, standard deviation of mean absolute value,
and a directional sensitivity value calculated as the covariance between
feature values and SHAP values divided by the variance of feature values.
This directional metric provides information about the nature of the
relationship between each variable and model predictions. To summarize the
SHAP values calculated, three different metrics are computed:<ul>
<li>Mean Absolute Value</li>
<li>Standard Deviation of Mean Absolute Value</li>
<li>Directional Sensitivity Value (Cov(Feature values, SHAP values) / Var(Feature values))</li>
</ul>

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

table_shap_results 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 the table.</dd></dl>

Arguments

SHAP Summarized Results Table — table_shap_results

<dl>

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


<dt>show_table</dt>
<dd>Boolean. Whether to print the table.</dd>

</dl>

table_shap_results: SHAP Summarized Results Table

Description

Usage

Value

Arguments

See Also

Examples