What are the differences between Gini importance and permutation importance?

What are the differences between Gini importance and permutation importance?

Gini importance measures feature importance based on the impurity decrease in decision trees, while permutation importance evaluates the impact of shuffling feature values on model performance.

Can you explain how Gini importance is calculated?

Gini importance is calculated by looking at the total decrease in node impurity (Gini impurity) brought by a feature across all trees in a forest.

How does permutation importance work?

Permutation importance works by randomly shuffling the values of a feature and measuring the change in the model's performance, indicating the feature's importance.

Which method is generally preferred?

It depends on the context; Gini importance is faster and works well with tree-based models, while permutation importance is model-agnostic and provides a more accurate measure.

Are there any limitations to these methods?

Yes, Gini importance can be biased towards features with more categories, while permutation importance can be computationally expensive and sensitive to correlated features.