diff --git a/Orange/widgets/visualize/ownomogram.py b/Orange/widgets/visualize/ownomogram.py
index d41d9ea0a88..c1c24b6007d 100644
--- a/Orange/widgets/visualize/ownomogram.py
+++ b/Orange/widgets/visualize/ownomogram.py
@@ -910,8 +910,7 @@ def calculate_log_reg_coefficients(self):
         if not isinstance(self.classifier, LogisticRegressionClassifier):
             return
 
-        self.domain = self.reconstruct_domain(self.classifier.original_domain,
-                                              self.domain)
+        self.domain = self.reconstruct_domain(self.classifier, self.domain)
         self.data = self.classifier.original_data.transform(self.domain)
         attrs, ranges, start = self.domain.attributes, [], 0
         for attr in attrs:
@@ -1273,8 +1272,9 @@ def send_report(self):
         self.report_plot()
 
     @staticmethod
-    def reconstruct_domain(original, preprocessed):
+    def reconstruct_domain(classifier: Model, preprocessed: Domain) -> Domain:
         # abuse dict to make "in" comparisons faster
+        original = classifier.original_domain
         attrs = OrderedDict()
         for attr in preprocessed.attributes:
             cv = attr._compute_value.variable._compute_value
@@ -1284,7 +1284,13 @@ def reconstruct_domain(original, preprocessed):
                 continue
             attrs[var] = None   # we only need keys
         attrs = list(attrs.keys())
-        return Domain(attrs, original.class_var, original.metas)
+
+        orig_clv = original.class_var
+        orig_data = classifier.original_data
+        values = (orig_clv.values[int(i)] for i in
+                  np.unique(orig_data.get_column_view(orig_clv)[0]))
+        class_var = DiscreteVariable(original.class_var.name, values)
+        return Domain(attrs, class_var, original.metas)
 
     @staticmethod
     def get_ruler_values(start, stop, max_width, round_to_nearest=True):
diff --git a/Orange/widgets/visualize/tests/test_ownomogram.py b/Orange/widgets/visualize/tests/test_ownomogram.py
index 538dfb0e5bc..87bb0e07c4b 100644
--- a/Orange/widgets/visualize/tests/test_ownomogram.py
+++ b/Orange/widgets/visualize/tests/test_ownomogram.py
@@ -14,6 +14,7 @@
 from Orange.preprocess import Scale, Continuize
 from Orange.tests import test_filename
 from Orange.widgets.tests.base import WidgetTest
+from Orange.widgets.tests.utils import simulate
 from Orange.widgets.visualize.ownomogram import (
     OWNomogram, DiscreteFeatureItem, ContinuousFeatureItem, ProbabilitiesDotItem,
     MovableToolTip
@@ -293,25 +294,52 @@ def test_dots_stop_flashing(self):
     def test_reconstruct_domain(self):
         data = Table("heart_disease")
         cls = LogisticRegressionLearner()(data)
-        domain = OWNomogram.reconstruct_domain(cls.original_domain, cls.domain)
+        domain = OWNomogram.reconstruct_domain(cls, cls.domain)
         transformed_data = cls.original_data.transform(domain)
         self.assertEqual(transformed_data.X.shape, data.X.shape)
         self.assertFalse(np.isnan(transformed_data.X[0]).any())
 
         scaled_data = Scale()(data)
         cls = LogisticRegressionLearner()(scaled_data)
-        domain = OWNomogram.reconstruct_domain(cls.original_domain, cls.domain)
+        domain = OWNomogram.reconstruct_domain(cls, cls.domain)
         transformed_data = cls.original_data.transform(domain)
         self.assertEqual(transformed_data.X.shape, scaled_data.X.shape)
         self.assertFalse(np.isnan(transformed_data.X[0]).any())
 
         disc_data = Continuize()(data)
         cls = LogisticRegressionLearner()(disc_data)
-        domain = OWNomogram.reconstruct_domain(cls.original_domain, cls.domain)
+        domain = OWNomogram.reconstruct_domain(cls, cls.domain)
         transformed_data = cls.original_data.transform(domain)
         self.assertEqual(transformed_data.X.shape, disc_data.X.shape)
         self.assertFalse(np.isnan(transformed_data.X[0]).any())
 
+    def test_missing_class_value(self):
+        iris = Table("iris")
+        iris_set_ver = iris[:100]
+        target_cb = self.widget.controls.target_class_index
+
+        lr = LogisticRegressionLearner()(iris)
+        self.send_signal(self.widget.Inputs.classifier, lr)
+        simulate.combobox_activate_index(target_cb, 2)
+        self.assertEqual(target_cb.currentIndex(), 2)
+        self.assertEqual(target_cb.count(), 3)
+
+        lr = LogisticRegressionLearner()(iris_set_ver)
+        self.send_signal(self.widget.Inputs.classifier, lr)
+        self.assertEqual(target_cb.currentIndex(), 0)
+        self.assertEqual(target_cb.count(), 2)
+
+        nb = NaiveBayesLearner()(iris)
+        self.send_signal(self.widget.Inputs.classifier, nb)
+        simulate.combobox_activate_index(target_cb, 2)
+        self.assertEqual(target_cb.currentIndex(), 2)
+        self.assertEqual(target_cb.count(), 3)
+
+        nb = NaiveBayesLearner()(iris_set_ver)
+        self.send_signal(self.widget.Inputs.classifier, nb)
+        self.assertEqual(target_cb.currentIndex(), 2)
+        self.assertEqual(target_cb.count(), 3)
+
 
 if __name__ == "__main__":
     unittest.main()