diff --git a/Classification/include/CGAL/Classification/Trainer.h b/Classification/include/CGAL/Classification/Trainer.h
index 96c6d8f9c08..616c7f27e4c 100644
--- a/Classification/include/CGAL/Classification/Trainer.h
+++ b/Classification/include/CGAL/Classification/Trainer.h
@@ -47,6 +47,8 @@ public:
 private:
   Classifier* m_classifier;
   std::vector<std::vector<std::size_t> > m_training_sets;
+  std::vector<double> m_precision;
+  std::vector<double> m_recall;
 
 public:
 
@@ -362,6 +364,57 @@ public:
     return Label_handle();
   }
 
+  void compute_precision_recall ()
+  {
+    std::vector<std::size_t> true_positives (m_classifier->number_of_labels());
+    std::vector<std::size_t> false_positives (m_classifier->number_of_labels());
+    std::vector<std::size_t> false_negatives (m_classifier->number_of_labels());
+
+    for (std::size_t j = 0; j < m_classifier->number_of_labels(); ++ j)
+      {
+        for (std::size_t k = 0; k < m_training_sets[j].size(); ++ k)
+          {
+            std::size_t nb_class_best=0; 
+            double val_class_best = (std::numeric_limits<double>::max)();
+      
+            for(std::size_t l = 0; l < m_classifier->number_of_labels(); ++ l)
+              {
+                double value = m_classifier->classification_value (m_classifier->label(l),
+                                                                   m_training_sets[j][k]);
+          
+                if(val_class_best > value)
+                  {
+                    val_class_best = value;
+                    nb_class_best = l;
+                  }
+              }
+
+            if (nb_class_best == j)
+              ++ true_positives[j];
+            else
+              {
+                ++ false_negatives[j];
+                for(std::size_t l = 0; l < m_classifier->number_of_labels(); ++ l)
+                  if (nb_class_best == l)
+                    ++ false_positives[l];
+              }
+          }
+      }
+
+
+    m_precision.clear();
+    m_recall.clear();
+
+    for (std::size_t j = 0; j < m_classifier->number_of_labels(); ++ j)
+      {
+        m_precision.push_back (true_positives[j] / double(true_positives[j] + false_positives[j]));
+        m_recall.push_back (true_positives[j] / double(true_positives[j] + false_negatives[j]));
+        std::cerr << m_classifier->label(j)->name() << ": " << std::endl
+                  << " * precision = " << m_precision.back() << std::endl
+                  << " * recall = " << m_recall.back() << std::endl
+                  << " * F_1 = " << 2. * (m_precision.back() * m_recall.back()) / (m_precision.back() + m_recall.back()) << std::endl;
+      }
+  }
   /// \endcond
 
 private: