What are the best practices for determining feature importance in machine learning?

What are the best practices for determining feature importance in machine learning?

Some best practices include using model-specific methods, cross-validation, and feature selection techniques.

Can you explain model-specific methods?

Sure! Model-specific methods like permutation importance and SHAP values provide insights tailored to the model's behavior.

What is permutation importance?

Permutation importance measures the change in model performance when the values of a feature are randomly shuffled.

How does SHAP differ from permutation importance?

SHAP values provide a unified measure of feature importance based on game theory, offering more interpretability.