diff --git a/src/conformist/performance_report.py b/src/conformist/performance_report.py
index 7405332..e751211 100644
--- a/src/conformist/performance_report.py
+++ b/src/conformist/performance_report.py
@@ -33,66 +33,65 @@ def pct_trio_plus_sets(prediction_sets):
                    prediction_set in prediction_sets) / \
                     len(prediction_sets)
 
-    def visualize_mean_set_sizes_by_class(self,
-                                          mean_set_sizes_by_class):
+    def _class_report(self,
+                      items_by_class,
+                      output_file_prefix,
+                      ylabel,
+                      color):
         # Reset plt
         plt.figure()
 
         # Sort the dictionary by its values
-        mean_set_sizes = dict(sorted(mean_set_sizes_by_class.items(),
-                                     key=lambda item: item[1]))
+        mean_sizes = dict(sorted(items_by_class.items(),
+                                 key=lambda item: item[1]))
 
         # Convert dictionary to dataframe and transpose
-        df = pd.DataFrame(mean_set_sizes, index=[0]).T
+        df = pd.DataFrame(mean_sizes, index=[0]).T
 
         # Save as csv
-        df.to_csv(f'{self.output_dir}/mean_set_sizes_class.csv',
+        df.to_csv(f'{self.output_dir}/{output_file_prefix}.csv',
                   index=True, header=False)
 
         # Visualize this dict as a bar chart
         sns.set_style('whitegrid')
-        palette = sns.color_palette("deep")
         fig, ax = plt.subplots(figsize=(10, 6))
-        ax.bar(range(len(mean_set_sizes)), mean_set_sizes.values(), color=palette[1])
-        ax.set_xticks(range(len(mean_set_sizes)))
-        ax.set_xticklabels(mean_set_sizes.keys(), rotation='vertical')
-        ax.set_ylabel('Mean set size')
+        ax.bar(range(len(mean_sizes)), mean_sizes.values(), color=color)
+        ax.set_xticks(range(len(mean_sizes)))
+        ax.set_xticklabels(mean_sizes.keys(), rotation='vertical')
+        ax.set_ylabel(ylabel)
         ax.set_xlabel('True class')
         plt.tight_layout()
-        plt.savefig(f'{self.output_dir}/mean_set_sizes_by_class.png')
+        plt.savefig(f'{self.output_dir}/{output_file_prefix}.png')
+
+    def visualize_mean_set_sizes_by_class(self,
+                                          mean_set_sizes_by_class):
+        palette = sns.color_palette("deep")
+        self._class_report(mean_set_sizes_by_class,
+                           'mean_set_sizes_by_class',
+                           'Mean set size',
+                           palette[1])
 
     def visualize_mean_fnrs_by_class(self,
                                      mean_fnrs_by_class):
-        # Setup
-        plt.figure()
-
-        # Sort the dictionary by its values
-        mean_fnrs = dict(sorted(mean_fnrs_by_class.items(),
-                                key=lambda item: item[1]))
-
-        # Visualize this dict as a bar chart
-        sns.set_style('whitegrid')
         palette = sns.color_palette("deep")
-        fig, ax = plt.subplots(figsize=(10, 6))
-        ax.bar(range(len(mean_fnrs)), mean_fnrs.values(), color=palette[0])
-        ax.set_xticks(range(len(mean_fnrs)))
-        ax.set_xticklabels(mean_fnrs.keys(), rotation='vertical')
-        ax.set_ylabel('Mean FNR')
-        ax.set_xlabel('True class')
-        plt.tight_layout()
-
-        # Export as fig and text
-        plt.savefig(f'{self.output_dir}/mean_fnrs_by_class.png')
+        self._class_report(mean_fnrs_by_class,
+                           'mean_fnrs_by_class',
+                           'Mean FNR',
+                           palette[0])
 
-        # Convert dictionary to dataframe and transpose
-        df = pd.DataFrame(mean_fnrs, index=[0]).T
-
-        # Save as csv
-        df.to_csv(f'{self.output_dir}/mean_fnrs_by_class.csv',
-                  index=True, header=False)
+    def visualize_mean_model_fnrs_by_class(self,
+                                           mean_fnrs_by_class):
+        palette = sns.color_palette("deep")
+        self._class_report(mean_fnrs_by_class,
+                           'mean_model_fnrs_by_class',
+                           'Mean model FNR',
+                           palette[2])
 
     def report_class_statistics(self,
                                 mean_set_sizes_by_class,
-                                mean_fnrs_by_class):
+                                mean_fnrs_by_class,
+                                mean_model_fnrs_by_class=None):
         self.visualize_mean_fnrs_by_class(mean_fnrs_by_class)
         self.visualize_mean_set_sizes_by_class(mean_set_sizes_by_class)
+        if mean_model_fnrs_by_class:
+            self.visualize_mean_model_fnrs_by_class(mean_model_fnrs_by_class)
diff --git a/src/conformist/validation_run.py b/src/conformist/validation_run.py
index e2935bc..8bb2d38 100644
--- a/src/conformist/validation_run.py
+++ b/src/conformist/validation_run.py
@@ -93,13 +93,13 @@ def mean_set_sizes_by_class(self, class_names):
             averages[key] = sum(sizes) / len(sizes)
         return averages
 
-    def mean_fnrs_by_class(self, class_names):
+    def mean_fnrs_by_class(self, sets, class_names):
         fnrs = {}
-        for i in range(len(self.prediction_sets)):
+        for i in range(len(sets)):
             labels = self.labels_idx[i]
             # Get corresponding values from class_names
             pset_class_names = [class_names[i] for i, label in enumerate(labels) if label == 1]
-            pset = np.array([int(value) for value in self.prediction_sets[i]])
+            pset = np.array([int(value) for value in sets[i]])
             if (pset[labels == 1] == 1).size > 0:
                 fnr = 1 - np.mean((pset[labels == 1] == 1))
             else:
@@ -117,7 +117,8 @@ def mean_fnrs_by_class(self, class_names):
     def run_reports(self, base_output_dir):
         pr = PerformanceReport(base_output_dir)
         pr.report_class_statistics(self.mean_set_sizes_by_class(self.class_names),
-                                   self.mean_fnrs_by_class(self.class_names))
+                                   self.mean_fnrs_by_class(self.prediction_sets, self.class_names),
+                                   self.mean_fnrs_by_class(self.model_predictions, self.class_names))
 
         np.seterr(all='raise')
         self.create_output_dir(base_output_dir)