  Current practice in interpretable machine learning often focuses on
explaining the final model trained from data, e.g., by using the Shapley
additive explanations (SHAP) method. The recently developed Shapley variable
importance cloud (ShapleyVIC) extends the current practice to a group of
"nearly optimal models" to provide comprehensive and robust variable importance
assessments, with estimated uncertainty intervals for a more complete
understanding of variable contributions to predictions. ShapleyVIC was
initially developed for applications with traditional regression models, and
the benefits of ShapleyVIC inference have been demonstrated in real-life
prediction tasks using the logistic regression model. However, as a
model-agnostic approach, ShapleyVIC application is not limited to such
scenarios. In this work, we extend ShapleyVIC implementation for machine
learning models to enable wider applications, and propose it as a useful
complement to the current SHAP analysis to enable more trustworthy applications
of these black-box models.
