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 …
API Reference — SHAP latest documentation
This page contains the API reference for public objects and functions in SHAP. There are also example notebooks available that demonstrate how to use the API of each object/function.
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 · 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 …
Using SHAP Values to Explain How Your Machine Learning Model Works
Jan 17, 2022 · SHAP values (SH apley A dditive ex P lanations) is a method based on cooperative game theory and used to increase transparency and interpretability of machine learning models.
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 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 …
Adversarial Evasion Attacks on Computer Vision using SHAP Values
2 days ago · The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise the performance of deep learning …