What are some techniques for model interpretability?

What are some techniques for model interpretability?

There are several techniques for model interpretability, including LIME, SHAP, and feature importance analysis.

Can you explain LIME?

LIME, or Local Interpretable Model-agnostic Explanations, approximates a model locally with an interpretable one to explain predictions.

What about SHAP?

SHAP, or SHapley Additive exPlanations, assigns each feature an importance value for a particular prediction, based on cooperative game theory.

How do I choose between LIME and SHAP?

Choosing between LIME and SHAP depends on your needs; LIME is faster for local explanations, while SHAP provides consistent global insights.

Are there any other techniques I should know about?

Yes, other techniques include partial dependence plots, individual conditional expectation plots, and using interpretable models like decision trees.