feature.selection

This function specifies the top features and prepares the data
 for plotting SHAP contributions for each row, or summary of absolute
 SHAP contributions for each feature.

This R package introduces Weighted Mean SHapley Additive exPlanations (WMSHAP), an innovative method for calculating SHAP values for a grid of fine-tuned base-learner machine learning models as well as stacked ensembles, a method not previously available due to the common reliance on single best-performing models. By integrating the weighted mean SHAP values from individual base-learners comprising the ensemble or individual base-learners in a tuning grid search, the package weights SHAP contributions according to each model's performance, assessed by multiple either R squared (for both regression and classification models). alternatively, this software also offers weighting SHAP values based on the area under the precision-recall curve (AUCPR), the area under the curve (AUC), and F2 measures for binary classifiers. It further extends this framework to implement weighted confidence intervals for weighted mean SHAP values, offering a more comprehensive and robust feature importance evaluation over a grid of machine learning models, instead of solely computing SHAP values for the best model. This methodology is particularly beneficial for addressing the severe class imbalance (class rarity) problem by providing a transparent, generalized measure of feature importance that mitigates the risk of reporting SHAP values for an overfitted or biased model and maintains robustness under severe class imbalance, where there is no universal criteria of identifying the absolute best model. Furthermore, the package implements hypothesis testing to ascertain the statistical significance of SHAP values for individual features, as well as comparative significance testing of SHAP contributions between features. Additionally, it tackles a critical gap in feature selection literature by presenting criteria for the automatic feature selection of the most important features across a grid of models or stacked ensembles, eliminating the need for arbitrary determination of the number of top features to be extracted. This utility is invaluable for researchers analyzing feature significance, particularly within severely imbalanced outcomes where conventional methods fall short. Moreover, it is also expected to report democratic feature importance across a grid of models, resulting in a more comprehensive and generalizable feature selection. The package further implements a novel method for visualizing SHAP values both at subject level and feature level as well as a plot for feature selection based on the weighted mean SHAP ratios.

E. F. Haghish 

shapley

Weighted Mean SHAP and CI for Robust Feature Assessment in ML
Grid

feature.selection function

<dl><dt>shapley</dt>
<dd>shapley object</dd>
<dt>method</dt>
<dd>Character. The column name in <code>summaryShaps</code> used
for feature selection. Default is <code>"mean"</code>, which
selects important features which have weighted mean shap
ratio (WMSHAP) higher than the specified cutoff. Other
alternative is "lowerCI", which selects features which
their lower bound of confidence interval is higher than
the cutoff.</dd>
<dt>cutoff</dt>
<dd>numeric, specifying the cutoff for the method used for selecting
the top features. the default is zero, which means that all
features with the "method" criteria above zero will be selected.</dd>
<dt>top_n_features</dt>
<dd>integer. if specified, the top n features with the
highest weighted SHAP values will be selected, overrullung
the 'cutoff' and 'method' arguments.</dd>
<dt>features</dt>
<dd>character vector, specifying the feature to be plotted.</dd></dl>

Arguments

Author

Selects the top features with highest weighted mean shap values based on the
       specified criteria — feature.selection

<dl>

<dt>shapley</dt>
<dd>shapley object</dd>


<dt>method</dt>
<dd>Character. The column name in <code>summaryShaps</code> used
for feature selection. Default is <code>"mean"</code>, which
selects important features which have weighted mean shap
ratio (WMSHAP) higher than the specified cutoff. Other
alternative is "lowerCI", which selects features which
their lower bound of confidence interval is higher than
the cutoff.</dd>


<dt>cutoff</dt>
<dd>numeric, specifying the cutoff for the method used for selecting
the top features. the default is zero, which means that all
features with the "method" criteria above zero will be selected.</dd>


<dt>top_n_features</dt>
<dd>integer. if specified, the top n features with the
highest weighted SHAP values will be selected, overrullung
the 'cutoff' and 'method' arguments.</dd>


<dt>features</dt>
<dd>character vector, specifying the feature to be plotted.</dd>

</dl>

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feature.selection: Selects the top features with highest weighted mean shap values based on the specified criteria

Description

Usage

Value

Arguments

Author