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)
Frontiers Integration of shapley additive explanations with
SHAP: Shapley Additive Explanations, by Fernando López
Algorithms, Free Full-Text
Measuring feature importance, removing correlated features, by Manish Chablani
A) Shapley additive explanations (SHAP) analysis for the 12
Shapley Additive Explanations (SHAP)
Debiasing SHAP scores in random forests
9.2 Local Surrogate (LIME) Interpretable Machine Learning
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Sensors, Free Full-Text
SHAP (SHapley Additive exPlanations), by Cory Maklin
Sensors, Free Full-Text
