ean of feature importance over `n_repeats`.
    importances_std : ndarray of shape (n_features, )
        Standard deviation over `n_repeats`.
    importances : ndarray of shape (n_features, n_repeats)
        Raw permutation importance scores.

    If there are multiple scoring metrics in the scoring parameter
    `result` is a dict with scorer names as keys (e.g. 'roc_auc') and
    `Bunch` objects like above as values.

References
----------
.. [BRE] :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
         2001. <10.1023/A:1010933404324>`

Examples
--------
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.inspection import permutation_importance
>>> X = [[1, 9, 9],[1, 9, 9],[1, 9, 9],
...      [0, 9, 9],[0, 9, 9],[0, 9, 9]]
>>> y = [1, 1, 1, 0, 0, 0]
>>> clf = LogisticRegression().fit(X, y)
>>> result = permutation_importance(clf, X, y, n_repeats=10,
...                                 random_state=0)
>>> result.importances_mean
array([0.4666..., 0.       , 0.       ])
>>> result.importances_std
array([0.2211..., 0.       , 0.       ])
r%