SHAP

SHAP (SHapley Additive exPlanations) is a Python library for interpreting machine learning model predictions by computing Shapley values from game theory. It provides a unified framework to explain the output of any machine learning model, attributing the prediction to each feature's contribution. This helps in understanding model behavior, identifying important features, and ensuring transparency in AI systems.

Also known as: Shapley Additive Explanations, Shapley values, SHAP values, Shapley, Shapley Additive exPlanations

Why learn SHAP?

Developers should learn SHAP when building or deploying machine learning models that require interpretability, such as in healthcare, finance, or regulatory compliance where explainability is crucial. It is particularly useful for debugging models, validating feature importance, and communicating insights to stakeholders, as it works with various model types including tree-based, deep learning, and linear models.

Learning Resources

• docs: SHAP Documentation
• tutorial: Interpretable Machine Learning with SHAP - Towards Data Science
• docs: SHAP: A Unified Approach to Interpreting Model Predictions - Paper
• course: Machine Learning Explainability with SHAP - Coursera Course
• video: SHAP Tutorial - YouTube Video by Data School