SHAP
SHAP (SHapley Additive exPlanations) is a game theory-based method for interpreting the output of machine learning models by attributing the prediction of each feature to its contribution. It provides a unified framework for model interpretability by calculating Shapley values, which measure the average marginal contribution of a feature across all possible combinations. This helps in understanding complex models like gradient boosting or neural networks by explaining individual predictions or overall feature importance.
Developers should learn SHAP when building or deploying machine learning models that require transparency and trust, such as in healthcare, finance, or regulatory compliance, where explaining predictions is critical. It is particularly useful for debugging models, identifying biases, and communicating results to non-technical stakeholders by providing intuitive, consistent explanations based on solid mathematical foundations.