What are some techniques for model interpretability?

What are some techniques for model interpretability?

Model interpretability techniques help us understand how machine learning models make decisions. Some common techniques include SHAP values, LIME, and feature importance analysis.

Can you explain SHAP values?

SHAP values, or SHapley Additive exPlanations, provide a way to explain the output of any machine learning model by attributing the prediction to each feature's contribution.

How does LIME differ from SHAP?

LIME, or Local Interpretable Model-agnostic Explanations, focuses on explaining individual predictions by approximating the model locally with an interpretable model, while SHAP provides a unified measure of feature importance.

Are there any tools available for these techniques?

Yes, there are several tools available, such as SHAP library for Python, LIME package, and various visualization tools that can help in interpreting model predictions.