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.
Shapley Additive Explanations (SHAP)
SHapley Additive exPlanations(SHAP): A Simple Explainer
Algorithms, Free Full-Text
Interpretable machine learning model for evaluating mechanical properties of concrete made with recycled concrete aggregate - Nguyen - Structural Concrete - Wiley Online Library
Shapley Additive Explanations — InterpretML documentation
Putt for Dough: The Golf Shot That Pays Best According to Machine Learning, by Ben Jensen, CodeX
Interpretable Machine… by Christoph Molnar [PDF/iPad/Kindle]
Measuring feature importance, removing correlated features, by Manish Chablani
MAKE, Free Full-Text
Explaining Machine Learning Models: A Non-Technical Guide to
Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings - ScienceDirect
SHAP (SHapley additive exPlanations) framework for the features in
SHAP: Shapley Additive Explanations, by Fernando López
SHAP: Shapley Additive Explanations - Arize AI