GitHub - shap/shap: A game theoretic approach to explain the output …
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 …
SHAP : A Comprehensive Guide to SHapley Additive exPlanations
Jul 14, 2025 · 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 …
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 …
shap · PyPI
Nov 11, 2025 · 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 …
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 …
An Introduction to SHAP Values and Machine Learning Interpretability
Jun 28, 2023 · SHAP (SHapley Additive exPlanations) values are a way to explain the output of any machine learning model. It uses a game theoretic approach that measures each player's contribution …
SHAP: Shapley Additive Explanations - Towards Data Science
Jul 11, 2021 · SHAP and its variants are integrated into the python library shap , which, in addition to providing different methods for calculating Shapely values, also integrates several methods for the …
SHAP Values Explained - Medium
Sep 19, 2024 · SHAP (SHapley Additive exPlanations) is a powerful tool in the machine learning world that draws its roots from game theory. In simple terms, SHAP values allow you to break down a …
Shapley Additive Explanation - an overview - ScienceDirect
Shapley Additive Explanation (SHAP) is defined as a methodology that unifies model interpretability by assigning importance values to individual features in the context of specific predictions, thereby …
A Perspective on Explainable Artificial Intelligence Methods: SHAP and …
Jun 17, 2024 · SHapley Additive exPlanations (SHAP) and Local Interpretable Model Agnostic Explanation (LIME) are two widely used XAI methods, particularly with tabular data.