code: def explain_permutation_importance(estimator,
                                     vec=None,
                                     top=_TOP,
                                     target_names=None,  # ignored
                                     targets=None,  # ignored
                                     feature_names=None,
                                     feature_re=None,
                                     feature_filter=None,
                                     ):
      
      coef = estimator.feature_importances_
      coef_std = estimator.feature_importances_std_
      return get_feature_importance_explanation(estimator, vec, coef,
          coef_std=coef_std,
          feature_names=feature_names,
          feature_filter=feature_filter,
          feature_re=feature_re,
          top=top,
          description=DESCRIPTION_SCORE_DECREASE + estimator.caveats_,
          is_regression=isinstance(estimator.wrapped_estimator_, RegressorMixin),
      )
docs: Return an explanation of PermutationImportance.
  
      See :func:`eli5.explain_weights` for description of
      ``top``, ``feature_names``, ``feature_re`` and ``feature_filter``
      parameters.
  
      ``target_names`` and ``targets`` parameters are ignored.
  
      ``vec`` is a vectorizer instance used to transform
      raw features to the input of the estimator (e.g. a fitted
      CountVectorizer instance); you can pass it instead of ``feature_names``.