Permutation variable importance measures how much a model’s prediction error increases when the values of a feature are randomly permuted. It is model-independent and helps estimate how much a feature contributes to predictive performance.
A type of variable importance that evaluates the increase in the prediction error of a model after permuting the feature's values. Permutation variable importance is a model-independent metric.