What is permutation importance?

Can you explain what permutation importance is in machine learning?

Sure! Permutation importance is a technique used to determine the importance of individual features in a predictive model by measuring the change in model performance when the values of a feature are randomly shuffled.

How is it different from other feature importance methods?

Unlike methods like feature importance from tree-based models, permutation importance evaluates the impact of each feature on the model's performance directly, making it model-agnostic.

Can you give an example of how to calculate it?

Certainly! To calculate permutation importance, you first measure the model's accuracy on the validation set. Then, for each feature, you shuffle its values and measure the accuracy again. The drop in accuracy indicates the feature's importance.

Are there any limitations to using permutation importance?

Yes, permutation importance can be biased if features are correlated, as shuffling one feature may affect others. It's also computationally intensive.