shapley: Weighted Mean SHAP for Feature Selection in ML Grid and Ensemble
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.
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