Explainable AI (SHAP, LIME Overview)

Summary

Techniques for explaining an individual model prediction — SHAP and LIME estimate how much each feature contributed to a specific prediction, crucial for trust in high-stakes decisions (credit, healthcare, hiring).

• 1Apply SHAP to a trained model and interpret the output for a single prediction.
• 2Learn the conceptual difference between global explainability (the whole model) and local (one prediction).
• 3Understand why regulated industries often require this kind of explainability by law.