diff --git a/skorch/callbacks/scoring.py b/skorch/callbacks/scoring.py
index 23c1782c1..4700fa999 100644
--- a/skorch/callbacks/scoring.py
+++ b/skorch/callbacks/scoring.py
@@ -100,7 +100,7 @@ def convert_sklearn_metric_function(scoring):
 
         # those are scoring objects returned by make_scorer starting
         # from sklearn 0.22
-        scorer_names = ('_PredictScorer', '_ProbaScorer', '_ThresholdScorer')
+        scorer_names = ('_PredictScorer', '_ProbaScorer', '_ThresholdScorer', '_Scorer')
         if (
                 hasattr(module, 'startswith') and
                 module.startswith('sklearn.metrics.') and
diff --git a/skorch/classifier.py b/skorch/classifier.py
index b7f552eaf..82707367b 100644
--- a/skorch/classifier.py
+++ b/skorch/classifier.py
@@ -97,7 +97,7 @@ def classes_(self):
             if not len(self.classes):
                 raise AttributeError("{} has no attribute 'classes_'".format(
                     self.__class__.__name__))
-            return self.classes
+            return np.asarray(self.classes)
 
         try:
             return self.classes_inferred_
@@ -301,7 +301,7 @@ def _default_callbacks(self):
 
     @property
     def classes_(self):
-        return [0, 1]
+        return np.array([0, 1])
 
     # pylint: disable=signature-differs
     def check_data(self, X, y):
diff --git a/skorch/net.py b/skorch/net.py
index 32dfec7b2..1e1caa2b6 100644
--- a/skorch/net.py
+++ b/skorch/net.py
@@ -1597,7 +1597,7 @@ def predict_proba(self, X):
             yp = nonlin(yp)
             y_probas.append(to_numpy(yp))
         y_proba = np.concatenate(y_probas, 0)
-        return y_proba
+        return y_proba.astype(np.float64)
 
     def predict(self, X):
         """Where applicable, return class labels for samples in X.