Intro to SHAP, LIME and Model Interpretability
This content explains the importance of machine learning interpretability and methodologies for achieving it, using the LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) approaches. The tutorial demonstrates how to interpret a Random Forest model trained on a mobile dataset. The LIME method generates local explanations for individual predictions by creating an interpretable model. The SHAP method calculates values for each feature, measuring its contribution to the prediction’s outcome, and providing a comprehensive feature importance assessment.