The ShapleyVIC framework is now implemented using a Python library that trains the optimal model, generates nearly optimal models and evaluate Shapley-based variable importance from such models, and an R package that pools information across models to generate summary statistics and visualizations for inference.
Apr 8, 2022 · The resulting Shapley variable importance cloud consists of Shapley-based importance measures from each good model and pools information across models.
Dec 16, 2022 · The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to ...
Oct 6, 2021 · A Shapley variable importance cloud that pools information across good models to avoid biased assessments in SHAP analyses of final models.
The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to provide ...
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A Shapley variable importance cloud that pools information across good models to avoid biased assessments in SHAP analyses of final models is proposed, ...
Shapley variable importance cloud for interpretable machine learning.
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The resulting Shapley variable importance cloud consists of Shapley-based importance measures from each good model and pools information across models to ...
Oct 22, 2024 · The resulting Shapley variable importance cloud consists of Shapley-based importance measures from each good model and pools information across ...
SHAP importance offers important insight about the predictions created in experiments. It can help you understand which features are the most important to the ...
Following the VIC framework, our proposed ShapleyVIC extends the widely used Shapley-based variable importance measures beyond final models for a comprehensive ...