diff --git a/src/pg/test/expected/06_segmentation_test.out b/src/pg/test/expected/06_segmentation_test.out
index aa8944a..069b13b 100644
--- a/src/pg/test/expected/06_segmentation_test.out
+++ b/src/pg/test/expected/06_segmentation_test.out
@@ -1,7 +1,4 @@
 \pset format unaligned
-\set ECHO all
-\i test/fixtures/ml_values.sql
-SET client_min_messages TO WARNING;
 \set ECHO none
 _cdb_random_seeds
 
diff --git a/src/py/crankshaft/crankshaft/segmentation/segmentation.py b/src/py/crankshaft/crankshaft/segmentation/segmentation.py
index c5990f8..8caf055 100644
--- a/src/py/crankshaft/crankshaft/segmentation/segmentation.py
+++ b/src/py/crankshaft/crankshaft/segmentation/segmentation.py
@@ -90,7 +90,6 @@ def create_and_predict_segment(query, variable, target_query, model_params):
 
     ## extract column names to be used in building the segmentation model
     feature_columns = set(columns) - set([variable, 'cartodb_id', 'the_geom', 'the_geom_webmercator'])
-
     ## get data from database
     target, features = get_data(variable, feature_columns, query)
 
diff --git a/src/py/crankshaft/test/test_segmentation.py b/src/py/crankshaft/test/test_segmentation.py
index 7e806ff..d02e8b1 100644
--- a/src/py/crankshaft/test/test_segmentation.py
+++ b/src/py/crankshaft/test/test_segmentation.py
@@ -26,8 +26,6 @@ class SegmentationTest(unittest.TestCase):
     def test_replace_nan_with_mean(self):
         test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
 
-
-
     def test_create_and_predict_segment(self):
         n_samples = 1000
 
@@ -36,6 +34,7 @@ class SegmentationTest(unittest.TestCase):
         training_data = self.generate_random_data(n_samples, random_state_train)
         test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
 
+
         ids =  [{'cartodb_ids': range(len(test_data))}]
         rows =  [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
 
@@ -44,7 +43,6 @@ class SegmentationTest(unittest.TestCase):
         plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
         plpy._define_result('.*select \* from test.*' ,test_data)
 
-
         model_parameters =  {'n_estimators': 1200,
                              'max_depth': 3,
                              'subsample' : 0.5,
@@ -53,7 +51,7 @@ class SegmentationTest(unittest.TestCase):
 
         result = segmentation.create_and_predict_segment(
                 'select * from training',
-                'y',
+                'target',
                 'select * from test',
                 model_parameters)