diff --git a/source/experiments/train_on_reduced_dataset.py b/source/experiments/train_on_reduced_dataset.py
index 8748383cbee9666044da5dddf645106bc4d24be8..2ed0c9bc11d529507584f70ba15bad74c5a737a1 100644
--- a/source/experiments/train_on_reduced_dataset.py
+++ b/source/experiments/train_on_reduced_dataset.py
@@ -77,7 +77,7 @@ class RedAQbench:
         total_contribs = df.loc['total', :]
 
         # a histogram of the contributions
-        if False:
+        if True:
             plt.hist(total_contribs, bins=100)
             plt.xlabel('Sum of contributions over test set')
             plt.ylabel('Number of stations with that contribution sum in training set')
@@ -117,11 +117,11 @@ class RedAQbench:
         Let's see how the performance drops.
         """
         print('Train reference and reduced...')
-        ref_model_dir = f'reference_{model}/'
-        red_model_dir = f'reduced_{model}/'
-        if model == 'rf':
+        ref_model_dir = f'reference_{self.model}/' #Scarlet ??
+        red_model_dir = f'reduced_{self.model}/' #Scarlet ??
+        if self.model == 'rf':
             Model = RandomForest
-        elif model == 'nn':
+        elif self.model == 'nn':
             Model = NeuralNetwork
 
         # Train reference
@@ -246,10 +246,11 @@ def compare_unimportant_stations():
     # found 154 of only nn
     # found 153 of only rf
     # found 181 of both
+    pdb.set_trace()
 
 if __name__ == '__main__':
     """
     Conduct the experiment.
     """
-    # training_experiment()
+    training_experiment()
     compare_unimportant_stations()