diff --git a/proglearn/base.py b/proglearn/base.py
index d3bf5e718c..84a1a12629 100644
--- a/proglearn/base.py
+++ b/proglearn/base.py
@@ -133,6 +133,17 @@ def predict(self, X):
             Input data matrix. 
         """
         pass
+    
+    @abc.abstractmethod
+    def is_fitted(self):
+        """
+        Indicates whether the decider is fitted.
+        
+        Parameters
+        ----------
+        None
+        """
+        pass
 
 
 class ClassificationDecider(BaseDecider):
@@ -229,4 +240,4 @@ def predict_proba(self, X, task_id):
         task_id : obj
             The task on which you are interested in estimating posteriors.
         """
-        pass
\ No newline at end of file
+        pass
diff --git a/proglearn/deciders.py b/proglearn/deciders.py
index 9a3f6870d0..ccde5aa299 100755
--- a/proglearn/deciders.py
+++ b/proglearn/deciders.py
@@ -25,6 +25,7 @@ class SimpleAverage(ClassificationDecider):
 
     def __init__(self, classes=[]):
         self.classes = classes
+        self._is_fitted = False
 
     def fit(
         self,
@@ -43,6 +44,8 @@ def fit(
             self.classes = np.array(self.classes)
         self.transformer_id_to_transformers = transformer_id_to_transformers
         self.transformer_id_to_voters = transformer_id_to_voters
+        
+        self._is_fitted = True
         return self
 
     def predict_proba(self, X, transformer_ids=None):
@@ -67,5 +70,19 @@ def predict_proba(self, X, transformer_ids=None):
         return np.mean(vote_per_transformer_id, axis=0)
 
     def predict(self, X, transformer_ids=None):
+        if not self.is_fitted():
+            msg = (
+                "This %(name)s instance is not fitted yet. Call 'fit' with "
+                "appropriate arguments before using this decider."
+            )
+            raise NotFittedError(msg % {"name": type(self).__name__})
+        
         vote_overall = self.predict_proba(X, transformer_ids=transformer_ids)
         return self.classes[np.argmax(vote_overall, axis=1)]
+    
+    def is_fitted(self):
+        """
+        Doc strings here.
+        """
+
+        return self._is_fitted