diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 1cd98be..392f866 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -207,7 +207,7 @@ class ACC(AggregativeQuantifier):
             class_count = data.counts()
 
             # fit the learner on all data
-            self.learner.fit(*data.Xy)
+            self.learner, _ = training_helper(self.learner, data, fit_learner, val_split=None)
 
         else:
             self.learner, val_data = training_helper(self.learner, data, fit_learner, val_split=val_split)
@@ -294,7 +294,7 @@ class PACC(AggregativeProbabilisticQuantifier):
             y_ = np.vstack(y_)
 
             # fit the learner on all data
-            self.learner.fit(*data.Xy)
+            self.learner, _ = training_helper(self.learner, data, fit_learner, ensure_probabilistic=True, val_split=None)
 
         else:
             self.learner, val_data = training_helper(
diff --git a/quapy/plot.py b/quapy/plot.py
index 270fb80..0f5a0aa 100644
--- a/quapy/plot.py
+++ b/quapy/plot.py
@@ -12,7 +12,8 @@ plt.rcParams['figure.dpi'] = 200
 plt.rcParams['font.size'] = 16
 
 
-def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True, savepath=None):
+def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=None, show_std=True, legend=True,
+                    train_prev=None, savepath=None):
     fig, ax = plt.subplots()
     ax.set_aspect('equal')
     ax.grid()
@@ -33,6 +34,10 @@ def binary_diagonal(method_names, true_prevs, estim_prevs, pos_class=1, title=No
         if show_std:
             ax.fill_between(x_ticks, y_ave - y_std, y_ave + y_std, alpha=0.25)
 
+    if train_prev is not None:
+        train_prev = train_prev[pos_class]
+        ax.scatter(train_prev, train_prev, c='c', label='tr-prev', linewidth=2, edgecolor='k', s=100, zorder=3)
+
     ax.set(xlabel='true prevalence', ylabel='estimated prevalence', title=title)
     ax.set_ylim(0, 1)
     ax.set_xlim(0, 1)