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- 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
- An introduction to explainable AI with Shapley values — SHAP latest . . .
Shapley values are a widely used approach from cooperative game theory that come with desirable properties This tutorial is designed to help build a solid understanding of how to compute and interpet Shapley-based explanations of machine learning models
- 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)
- An Introduction to SHAP Values and Machine Learning Interpretability
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 to the final outcome
- 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) - AI Wiki
The open-source Python library shap, which has accumulated more than 25,000 stars on GitHub, provides implementations of several SHAP estimation algorithms (KernelSHAP, TreeSHAP, DeepSHAP, LinearSHAP, PermutationSHAP, and PartitionSHAP) along with a rich set of visualization tools
- 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
- Shapley Additive Explanation - an overview - ScienceDirect
SHAP explains the prediction of a data sample by calculating the contribution of each feature to the prediction of the algorithm The SHAP uses coalitional game theory to calculate Shapley values Shapley values show the distribution of prediction among features
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