diff --git a/openml/entities/dataset.py b/openml/entities/dataset.py
index c5c9f6bca..c3f14bcad 100644
--- a/openml/entities/dataset.py
+++ b/openml/entities/dataset.py
@@ -122,8 +122,7 @@ def decode_arff(fh):
                 return decode_arff(fh)
 
     ############################################################################
-    # pandas related stuff...
-    def get_dataset(self, target=None, include_row_id=False,
+    def get_dataset(self, target=None, target_dtype=int, include_row_id=False,
                    include_ignore_attributes=False,
                    return_categorical_indicator=False,
                    return_attribute_names=False):
@@ -176,7 +175,7 @@ def get_dataset(self, target=None, include_row_id=False,
 
             try:
                 x = data[:,~targets]
-                y = data[:,targets].astype(np.int32)
+                y = data[:,targets].astype(target_dtype)
 
                 if len(y.shape) == 2 and y.shape[1] == 1:
                     y = y[:,0]
@@ -191,7 +190,7 @@ def get_dataset(self, target=None, include_row_id=False,
                 raise e
 
             if scipy.sparse.issparse(y):
-                y = np.asarray(y.todense()).astype(np.int32).flatten()
+                y = np.asarray(y.todense()).astype(target_dtype).flatten()
 
             rval.append(x)
             rval.append(y)
diff --git a/openml/entities/task.py b/openml/entities/task.py
index 3ea72f59d..5609d8419 100644
--- a/openml/entities/task.py
+++ b/openml/entities/task.py
@@ -44,7 +44,14 @@ def get_dataset(self):
     def get_X_and_Y(self):
         dataset = self.get_dataset()
         # Replace with retrieve from cache
-        X_and_Y = dataset.get_dataset(target=self.target_feature)
+        if 'Supervised Classification'.lower() in self.task_type.lower():
+            target_dtype = int
+        elif 'Supervised Regression'.lower() in self.task_type.lower():
+            target_dtype = float
+        else:
+            raise NotImplementedError(self.task_type)
+        X_and_Y = dataset.get_dataset(target=self.target_feature,
+                                      target_dtype=target_dtype)
         return X_and_Y
 
     def evaluate(self, algo):