diff --git a/compiler/characterizer/regression_model.py b/compiler/characterizer/regression_model.py
index 98e7afe3..4a45bade 100644
--- a/compiler/characterizer/regression_model.py
+++ b/compiler/characterizer/regression_model.py
@@ -168,6 +168,9 @@ class regression_model(simulation):
     
     
     def cross_validation(self):
+        """Wrapper for sklean cross validation function for OpenRAM regression models.
+           Returns the mean accuracy for each model/output."""
+        
         from sklearn.model_selection import cross_val_score
         untrained_model = self.get_model()
         
@@ -179,13 +182,15 @@ class regression_model(simulation):
         output_num = 0
         models = {}
         debug.info(1, "Output name, mean_accuracy, std_dev")
+        model_scores = {}
         for o_name in self.output_names:
             output_label = labels[:,output_num]
-            scores = cross_val_score(untrained_model, features, output_label, cv=5)
+            scores = cross_val_score(untrained_model, features, output_label, cv=10)
             debug.info(1, "{}, {}, {}".format(o_name, scores.mean(), scores.std()))
+            model_scores[o_name] = scores.mean()
             output_num+=1
         
-            
+        return model_scores    
     
     # Fixme - only will work for sklearn regression models
     def save_model(self, model_name, model):
diff --git a/compiler/tests/21_regression_model_test.py b/compiler/tests/21_regression_model_test.py
index 2b2462c4..59665cab 100755
--- a/compiler/tests/21_regression_model_test.py
+++ b/compiler/tests/21_regression_model_test.py
@@ -30,6 +30,7 @@ class regression_model_test(openram_test):
         import characterizer
         reload(characterizer)
         from characterizer import linear_regression
+        from characterizer import neural_network
         from sram import sram
         from sram_config import sram_config
         c = sram_config(word_size=1,
@@ -49,31 +50,11 @@ class regression_model_test(openram_test):
 
         corner = (OPTS.process_corners[0], OPTS.supply_voltages[0], OPTS.temperatures[0])
         
-        m = linear_regression(s.s, tempspice, corner)
-        m.cross_validation()
-
-        # Only compare the delays
-        # spice_delays = {key:value for key, value in spice_data.items() if 'delay' in key}
-        # spice_delays['min_period'] = spice_data['min_period']
-        # model_delays = {key:value for key, value in model_data.items() if 'delay' in key}
-        # model_delays['min_period'] = model_data['min_period']
-        # debug.info(1,"Spice Delays={}".format(spice_delays))
-        # debug.info(1,"Model Delays={}".format(model_delays))
-
-        # if OPTS.tech_name == "freepdk45":
-            # error_tolerance = 0.25
-        # elif OPTS.tech_name == "scn4m_subm":
-            # error_tolerance = 0.25
-        # else:
-            # self.assertTrue(False) # other techs fail
-
-        # print('spice_delays', spice_delays)
-        # print('model_delays', model_delays)
-
-        # # Check if no too many or too few results
-        # self.assertTrue(len(spice_delays.keys())==len(model_delays.keys()))
-
-        # self.assertTrue(self.check_golden_data(spice_delays,model_delays,error_tolerance))
+        #m = linear_regression(s.s, tempspice, corner)
+        m = neural_network(s.s, tempspice, corner)
+        scores = m.cross_validation()
+        accuracy_requirement = 0.75
+        self.assertTrue(scores['rise_delay'] >= accuracy_requirement)
 
         globals.end_openram()