Welcome to the SHAP documentation SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations)
GitHub - shap shap: A game theoretic approach to explain the output of . . . SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations)
shap · PyPI SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations)
SHAP (SHapley Additive exPlanations): Complete Guide to Model . . . SHAP gives a unified framework that works directly across any machine learning model type Whether you're working with a simple linear regression, a random forest, a gradient boosting model, or a deep neural network, SHAP uses the same mathematical basis to explain predictions
18 SHAP – Interpretable Machine Learning - Christoph Molnar Looking for a comprehensive, hands-on guide to SHAP and Shapley values? Interpreting Machine Learning Models with SHAP has you covered With practical Python examples using the shap package, you’ll learn how to explain models ranging from simple to complex