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type: software
license: MIT
title: 'shapley: Weighted Mean SHAP for Feature Selection in ML Grid and Ensemble'
version: '0.4'
doi: 10.32614/CRAN.package.shapley
abstract: 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.
authors:
- name: E. F. Haghish
  email: haghish@hotmail.com
repository: https://CRAN.R-project.org/package=shapley
repository-code: https://github.com/haghish/shapley
url: https://www.sv.uio.no/psi/english/people/academic/haghish/
date-released: '2024-10-23'
contact:
- name: E. F. Haghish
  email: haghish@hotmail.com