diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 67b003a..a723dcc 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -729,6 +729,11 @@ class EMQ(AggregativeSoftQuantifier):
             posteriors = self.calibration_function(posteriors)
         return posteriors
 
+    def classifier_fit_predict(self, X, y):
+        classif_predictions = super().classifier_fit_predict(X, y)
+        self.train_prevalence = F.prevalence_from_labels(y, classes=self.classes_)
+        return classif_predictions
+
     def aggregation_fit(self, classif_predictions):
         """
         Trains the aggregation function of EMQ. This comes down to recalibrating the posterior probabilities
@@ -756,9 +761,7 @@ class EMQ(AggregativeSoftQuantifier):
                 y = np.searchsorted(self.classes_, y)
             self.calibration_function = calibrator(P, np.eye(n_classes)[y], posterior_supplied=True)
 
-        if self.exact_train_prev:
-            self.train_prevalence = F.prevalence_from_labels(y, self.classes_)
-        else:
+        if not self.exact_train_prev:
             train_posteriors = classif_predictions.X
             if self.recalib is not None:
                 train_posteriors = self.calibration_function(train_posteriors)