Permutation feature importance

The permutation feature importance is the decrease in a model score when a single feature value is randomly shuffled. The score function to be used for the computation of importances can be specified with the scoring argument, which also accepts multiple scorers. Using multiple scorers is more computationally efficient than sequentially calling permutation_importance several times with a different scorer, as it reuses model predictions.