9.6 SHAP (SHapley Additive exPlanations)

Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.

9.6 SHAP (SHapley Additive exPlanations)

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SHAP plots visualizing neural network identification of a normal

Interpretable Machine Learning: A Guide For Making Black Box Models Explainable: Molnar, Christoph: 9798411463330: : Books

SHAP (SHapley additive exPlanations) framework for the features in

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Extracting spatial effects from machine learning model using local

A novel approach to explain the black-box nature of machine

ML Explainability - Shapley Values & SHAP Library

Measuring feature importance, removing correlated features, by Manish Chablani

5.6 RuleFit Interpretable Machine Learning

Shapley additive explanations (SHAP) summary plot showing how the

SHAP feature importance measured as the mean absolute SHAP value