What are the different techniques for interpreting AI models?

What are the different techniques for interpreting AI models?

There are several techniques for interpreting AI models, including LIME, SHAP, and feature importance. Would you like to know more about a specific one?

Can you explain LIME and how it works?

LIME, or Local Interpretable Model-agnostic Explanations, works by approximating the model locally with an interpretable model. It perturbs the input data and observes the changes in predictions to understand the model's behavior.

What about SHAP? How does it differ from LIME?

SHAP, or SHapley Additive exPlanations, provides a unified measure of feature importance based on cooperative game theory. Unlike LIME, which focuses on local explanations, SHAP offers both local and global interpretability.

Which technique is generally preferred in practice?

It depends on the use case. SHAP is often preferred for its consistency and theoretical foundation, while LIME is valued for its simplicity and speed.