What are the advantages of using permutation importance in machine learning?

What are the advantages of using permutation importance in machine learning?

Permutation importance is a model-agnostic method that helps in understanding feature importance by measuring the change in model performance when the values of a feature are permuted.

How does it compare to other feature importance methods?

Unlike methods like Gini importance or coefficient values, permutation importance can be applied to any model and provides a more intuitive understanding of feature contributions.

Are there any limitations to using permutation importance?

Yes, permutation importance can be computationally expensive and may not perform well with highly correlated features, as it can lead to misleading results.

Can it be used for both classification and regression tasks?

Absolutely! Permutation importance is versatile and can be effectively used for both classification and regression models.