diff --git a/apt/minimization/minimizer.py b/apt/minimization/minimizer.py
index df70a3a..ebac318 100644
--- a/apt/minimization/minimizer.py
+++ b/apt/minimization/minimizer.py
@@ -62,9 +62,9 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
                  cells: Optional[list] = None, categorical_features: Optional[Union[np.ndarray, list]] = None,
                  features_to_minimize: Optional[Union[np.ndarray, list]] = None, train_only_QI: Optional[bool] = True,
                  is_regression: Optional[bool] = False):
-        if issubclass(estimator.__class__, Model):
-            self.estimator = estimator
-        else:
+
+        self.estimator = estimator
+        if estimator is not None and not issubclass(estimator.__class__, Model):
             if is_regression:
                 self.estimator = SklearnRegressor(estimator)
             else:
@@ -832,6 +832,7 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
         self._generalizations = {'ranges': GeneralizeToRepresentative._calculate_ranges(self.cells),
                                  'categories': GeneralizeToRepresentative._calculate_categories(self.cells),
                                  'untouched': GeneralizeToRepresentative._calculate_untouched(self.cells)}
+        self._remove_categorical_untouched(self._generalizations)
 
     def _find_range_count(self, samples, ranges):
         samples_df = pd.DataFrame(samples, columns=self._categorical_data.columns)
@@ -988,3 +989,17 @@ class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerM
                 del cell['categories'][feature]
             cell['untouched'].append(feature)
             cells_by_id[cell['id']] = cell.copy()
+
+    @staticmethod
+    def _remove_categorical_untouched(generalizations):
+        to_remove = []
+        for feature in generalizations['categories'].keys():
+            category_sizes = [len(g) if len(g) > 1 else 0 for g in generalizations['categories'][feature]]
+            if sum(category_sizes) == 0:
+                if 'untouched' not in generalizations:
+                    generalizations['untouched'] = []
+                generalizations['untouched'].append(feature)
+                to_remove.append(feature)
+
+        for feature in to_remove:
+            del generalizations['categories'][feature]
diff --git a/tests/test_minimizer.py b/tests/test_minimizer.py
index 181755b..6adad90 100644
--- a/tests/test_minimizer.py
+++ b/tests/test_minimizer.py
@@ -937,3 +937,25 @@ def test_blackbox_model():
     if len(expected_generalizations['ranges'].keys()) > 0 or len(expected_generalizations['categories'].keys()) > 0:
         assert (ncp > 0)
         assert (((transformed[indexes]) != (X[indexes])).any())
+
+
+def test_untouched():
+    cells = [{"id": 1, "ranges": {"age": {"start": None, "end": 38}}, "label": 0,
+              'categories': {'gender': ['male']}, "representative": {"age": 26, "height": 149}},
+             {"id": 2, "ranges": {"age": {"start": 39, "end": None}}, "label": 1,
+              'categories': {'gender': ['female']}, "representative": {"age": 58, "height": 163}},
+             {"id": 3, "ranges": {"age": {"start": None, "end": 38}}, "label": 0,
+              'categories': {'gender': ['male']}, "representative": {"age": 31, "height": 184}},
+             {"id": 4, "ranges": {"age": {"start": 39, "end": None}}, "label": 1,
+              'categories': {'gender': ['male', 'female']}, "representative": {"age": 45, "height": 176}}
+             ]
+    gen = GeneralizeToRepresentative(cells=cells)
+    gen._calculate_generalizations()
+    gener = gen.generalizations
+    expected_generalizations = {'ranges': {'age': [38, 39]}, 'categories': {}, 'untouched': ['gender']}
+    for key in expected_generalizations['ranges']:
+        assert (set(expected_generalizations['ranges'][key]) == set(gener['ranges'][key]))
+    for key in expected_generalizations['categories']:
+        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']))