Permutation importance is a technique used to measure the impact of individual features on the performance of a predictive model by evaluating how much the model's accuracy decreases when the values of that feature are randomly shuffled. This method provides insight into which features are most influential for the model’s predictions, aiding in model interpretation and explainability. By analyzing how the performance metric changes after permuting each feature, it becomes easier to identify the relative importance of different variables in the model's decision-making process.
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