content: """
  ================
  Confusion matrix
  ================
  
  Example of confusion matrix usage to evaluate the quality
  of the output of a classifier on the iris data set. The
  diagonal elements represent the number of points for which
  the predicted label is equal to the true label, while
  off-diagonal elements are those that are mislabeled by the
  classifier. The higher the diagonal values of the confusion
  matrix the better, indicating many correct predictions.
  
  The figures show the confusion matrix with and without
  normalization by class support size (number of elements
  in each class). This kind of normalization can be
  interesting in case of class imbalance to have a more
  visual interpretation of which class is being misclassified.
  
  Here the results are not as good as they could be as our
  choice for the regularization parameter C was not the best.
  In real life applications this parameter is usually chosen
  using :ref:`grid_search`.
  
  """
  
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn import svm, datasets
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import confusion_matrix
  
  # import some data to play with
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  
  # 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)
  
  # Run classifier, using a model that is too regularized (C too low) to see
  # the impact on the results
  classifier = svm.SVC(kernel='linear', C=0.01)
  y_pred = classifier.fit(X_train, y_train).predict(X_test)
  
  
  def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
      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)
      plt.tight_layout()
      plt.ylabel('True label')
      plt.xlabel('Predicted label')
  
  
  # Compute confusion matrix
  cm = confusion_matrix(y_test, y_pred)
  np.set_printoptions(precision=2)
  print('Confusion matrix, without normalization')
  print(cm)
  plt.figure()
  plot_confusion_matrix(cm)
  
  # 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)
  plt.figure()
  plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
  
  plt.show()
  