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- SHAP : A Comprehensive Guide to SHapley Additive exPlanations
SHAP (SHapley Additive exPlanations) has a variety of visualization tools that help interpret machine learning model predictions These plots highlight which features are important and also explain how they influence individual or overall model outputs
- 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)
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- An Introduction to SHAP Values and Machine Learning Interpretability
SHAP values can help you see which features are most important for the model and how they affect the outcome In this tutorial, we will learn about SHAP values and their role in machine learning model interpretation
- Using SHAP Values to Explain How Your Machine Learning Model Works
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
- Shapley value - Wikipedia
In cooperative game theory, the Shapley value is a method (solution concept) for fairly distributing the total gains or costs among a group of players who have collaborated For example, in a team project where each member contributed differently, the Shapley value provides a way to determine how much credit or blame each member deserves It was named in honor of Lloyd Shapley, who introduced
- Using SHAP values and IntegratedGradients for cell type classification . . .
Using SHAP values and IntegratedGradients for cell type classification interpretability # Previously we saw semi-supervised models, like SCANVI being used for tasks like cell type classification, enabling researchers to uncover complex biological patterns
- [PDF] Enhancing the Interpretability of SHAP Values Using Large . . .
This work uses the use of Large Language Models (LLMs) to translate SHAP value outputs into plain language explanations that are more accessible to non-technical audiences and enhances the overall interpretability of machine learning models Model interpretability is crucial for understanding and trusting the decisions made by complex machine learning models, such as those built with XGBoost
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