diff --git a/eli5/sklearn/permutation_importance.py b/eli5/sklearn/permutation_importance.py
index ca73d76..0c68cca 100644
--- a/eli5/sklearn/permutation_importance.py
+++ b/eli5/sklearn/permutation_importance.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 from sklearn.model_selection import check_cv
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils import check_array, check_random_state
 from sklearn.base import (
     BaseEstimator,
@@ -20,6 +20,12 @@
 if pandas_available:
     import pandas as pd
 
+def _estimator_has(attr):
+    def check(self):
+        return hasattr(self.wrapped_estimator_, attr)
+
+    return check
+
 CAVEATS_CV_NONE = """
 Feature importances are computed on the same data as used for training, 
 i.e. feature importances don't reflect importance of features for 
@@ -247,23 +253,23 @@ def caveats_(self):
 
     # ============= Exposed methods of a wrapped estimator:
 
-    @if_delegate_has_method(delegate='wrapped_estimator_')
+    @available_if(_estimator_has('score'))
     def score(self, X, y=None, *args, **kwargs):
         return self.wrapped_estimator_.score(X, y, *args, **kwargs)
 
-    @if_delegate_has_method(delegate='wrapped_estimator_')
+    @available_if(_estimator_has('predict'))
     def predict(self, X):
         return self.wrapped_estimator_.predict(X)
 
-    @if_delegate_has_method(delegate='wrapped_estimator_')
+    @available_if(_estimator_has('predict_proba'))
     def predict_proba(self, X):
         return self.wrapped_estimator_.predict_proba(X)
 
-    @if_delegate_has_method(delegate='wrapped_estimator_')
+    @available_if(_estimator_has('predict_log_proba'))
     def predict_log_proba(self, X):
         return self.wrapped_estimator_.predict_log_proba(X)
 
-    @if_delegate_has_method(delegate='wrapped_estimator_')
+    @available_if(_estimator_has('decision_function'))
     def decision_function(self, X):
         return self.wrapped_estimator_.decision_function(X)