21 - Permutation Feature Importance

Protect your feature explanations from cardinality bias by comparing native Mean Decrease Impurity (MDI) importance with Permutation Feature Importance in scikit-learn, numpy, and pandas. Using the California Housing dataset, we inject a high-cardinality random noise column to expose how native tree importances can be severely misleading. Learn how permutation testing on unseen test data provides an unbiased, model-agnostic measure of true feature impact.