Permutation Importance de la caractéristique

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Permutation Feature Importance

 These feature importances are based on the mean decrease in criterion, like gini impurity (for decision trees and random forests). It’s better to use permutation feature importances. With this method, the importances are based on measuring the increase of the prediction error when you permute the feature’s values. So you compute the prediction error two times, before and after permutation of the feature. The higher the difference between the prediction errors, the more important the feature.

Source : towardsdatascience