SHAP : A Comprehensive Guide to SHapley Additive exPlanations SHAP (SHapley Additive exPlanations) provides a robust and sound method to interpret model predictions by making attributes of importance scores to input features What is SHAP? SHAP is a method that helps us understand how a machine learning model makes decisions
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. Explainer — SHAP latest documentation This is the primary explainer interface for the SHAP library It takes any combination of a model and masker and returns a callable subclass object that implements the particular estimation algorithm that was chosen
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)
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