diff --git a/examples/model_selection/plot_confusion_matrix.py b/examples/model_selection/plot_confusion_matrix.py
index 250d71c08c442..233c72f658ba5 100644
--- a/examples/model_selection/plot_confusion_matrix.py
+++ b/examples/model_selection/plot_confusion_matrix.py
@@ -26,6 +26,7 @@
 
 print(__doc__)
 
+import itertools
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -37,6 +38,7 @@
 iris = datasets.load_iris()
 X = iris.data
 y = iris.target
+class_names = iris.target_names
 
 # Split the data into a training set and a test set
 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
@@ -47,32 +49,51 @@
 y_pred = classifier.fit(X_train, y_train).predict(X_test)
 
 
-def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
+def plot_confusion_matrix(cm, classes,
+                          normalize=False,
+                          title='Confusion matrix',
+                          cmap=plt.cm.Blues):
+    """
+    This function prints and plots the confusion matrix.
+    Normalization can be applied by setting `normalize=True`.
+    """
     plt.imshow(cm, interpolation='nearest', cmap=cmap)
     plt.title(title)
     plt.colorbar()
-    tick_marks = np.arange(len(iris.target_names))
-    plt.xticks(tick_marks, iris.target_names, rotation=45)
-    plt.yticks(tick_marks, iris.target_names)
+    tick_marks = np.arange(len(classes))
+    plt.xticks(tick_marks, classes, rotation=45)
+    plt.yticks(tick_marks, classes)
+
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+
+    print(cm)
+
+    thresh = cm.max() / 2.
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+        plt.text(j, i, cm[i, j],
+                 horizontalalignment="center",
+                 color="white" if cm[i, j] > thresh else "black")
+
     plt.tight_layout()
     plt.ylabel('True label')
     plt.xlabel('Predicted label')
 
-
 # Compute confusion matrix
-cm = confusion_matrix(y_test, y_pred)
+cnf_matrix = confusion_matrix(y_test, y_pred)
 np.set_printoptions(precision=2)
-print('Confusion matrix, without normalization')
-print(cm)
+
+# Plot non-normalized confusion matrix
 plt.figure()
-plot_confusion_matrix(cm)
+plot_confusion_matrix(cnf_matrix, classes=class_names,
+                      title='Confusion matrix, without normalization')
 
-# Normalize the confusion matrix by row (i.e by the number of samples
-# in each class)
-cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
-print('Normalized confusion matrix')
-print(cm_normalized)
+# Plot normalized confusion matrix
 plt.figure()
-plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
+plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
+                      title='Normalized confusion matrix')
 
 plt.show()