How do different feature importance methods compare?

How do different feature importance methods compare?

There are several methods to assess feature importance, including permutation importance, SHAP values, and tree-based importance. Each has its strengths and weaknesses.

Can you explain how SHAP values work?

SHAP values, or SHapley Additive exPlanations, provide a way to understand the contribution of each feature to the model's predictions by distributing the prediction value among the features.

What are the advantages of using permutation importance?

Permutation importance is model-agnostic and easy to implement. It measures the change in model performance when the values of a feature are randomly shuffled.

Are there any drawbacks to using tree-based importance?

Yes, tree-based importance can be biased towards features with more categories and may not reflect the true importance in some contexts.