diff --git a/apt/minimization/minimizer.py b/apt/minimization/minimizer.py
index 702b799..1e15d3d 100644
--- a/apt/minimization/minimizer.py
+++ b/apt/minimization/minimizer.py
@@ -93,8 +93,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
         self.train_only_features_to_minimize = train_only_features_to_minimize
         self.is_regression = is_regression
         self.encoder = encoder
-        # self.generalize_using_transform = generalize_using_transform
-        self.generalize_using_transform = False
+        self.generalize_using_transform = generalize_using_transform
         self._ncp = 0.0
         self._feature_data = {}
         self._categorical_values = {}
diff --git a/tests/test_minimizer.py b/tests/test_minimizer.py
index 6cc5197..8e5a6cc 100644
--- a/tests/test_minimizer.py
+++ b/tests/test_minimizer.py
@@ -75,7 +75,7 @@ def test_minimizer_params_not_transform(data):
     model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
     model.fit(ArrayDataset(X, y))
 
-    gen = GeneralizeToRepresentative(model, cells=cells)
+    gen = GeneralizeToRepresentative(model, cells=cells, generalize_using_transform=False)
     gen.calculate_ncp(X)
     ncp = gen.ncp
     assert (ncp > 0.0)
@@ -158,7 +158,7 @@ def test_minimizer_fit_not_transform(data):
     if predictions.shape[1] > 1:
         predictions = np.argmax(predictions, axis=1)
     target_accuracy = 0.5
-    gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy)
+    gen = GeneralizeToRepresentative(model, target_accuracy=target_accuracy, generalize_using_transform=False)
     train_dataset = ArrayDataset(X, predictions, features_names=features)
 
     gen.fit(dataset=train_dataset)
@@ -1043,3 +1043,32 @@ def test_untouched():
         assert (set([frozenset(sl) for sl in expected_generalizations['categories'][key]])
                 == set([frozenset(sl) for sl in gener['categories'][key]]))
     assert (set(expected_generalizations['untouched']) == set(gener['untouched']))
+
+
+def test_errors():
+    features = ['age', 'height']
+    X = np.array([[23, 165],
+                  [45, 158],
+                  [56, 123],
+                  [67, 154],
+                  [45, 149],
+                  [42, 166],
+                  [73, 172],
+                  [94, 168],
+                  [69, 175],
+                  [24, 181],
+                  [18, 190]])
+    y = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0])
+    base_est = DecisionTreeClassifier(random_state=0, min_samples_split=2,
+                                      min_samples_leaf=1)
+    model = SklearnClassifier(base_est, ModelOutputType.CLASSIFIER_PROBABILITIES)
+    model.fit(ArrayDataset(X, y))
+    ad = ArrayDataset(X)
+    predictions = model.predict(ad)
+    if predictions.shape[1] > 1:
+        predictions = np.argmax(predictions, axis=1)
+    gen = GeneralizeToRepresentative(model, generalize_using_transform=False)
+    train_dataset = ArrayDataset(X, predictions, features_names=features)
+    gen.fit(dataset=train_dataset)
+    with pytest.raises(ValueError):
+        gen.transform(X)