How can we improve interpretability in machine learning models?

How can we improve interpretability in machine learning models?

Improving interpretability can involve techniques like feature importance, LIME, SHAP, and using simpler models.

What is LIME and how does it work?

LIME stands for Local Interpretable Model-agnostic Explanations. It explains model predictions by approximating the model locally with an interpretable one.

Can you explain SHAP in a similar way?

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

Are there any best practices for using these techniques?

Yes, best practices include validating explanations with domain experts, using multiple methods for robustness, and ensuring user-friendly visualizations.