old_contents: """
  ================================================================
  Plot the decision surface of a decision tree on the iris dataset
  ================================================================
  
  Plot the decision surface of a decision tree trained on pairs
  of features of the iris dataset.
  
  See :ref:`decision tree <tree>` for more information on the estimator.
  
  For each pair of iris features, the decision tree learns decision
  boundaries made of combinations of simple thresholding rules inferred from
  the training samples.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.datasets import load_iris
  from sklearn.tree import DecisionTreeClassifier
  
  # Parameters
  n_classes = 3
  plot_colors = "bry"
  plot_step = 0.02
  
  # Load data
  iris = load_iris()
  
  for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
                                  [1, 2], [1, 3], [2, 3]]):
      # We only take the two corresponding features
      X = iris.data[:, pair]
      y = iris.target
  
      # Train
      clf = DecisionTreeClassifier().fit(X, y)
  
      # Plot the decision boundary
      plt.subplot(2, 3, pairidx + 1)
  
      x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                           np.arange(y_min, y_max, plot_step))
  
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
  
      plt.xlabel(iris.feature_names[pair[0]])
      plt.ylabel(iris.feature_names[pair[1]])
      plt.axis("tight")
  
      # Plot the training points
      for i, color in zip(range(n_classes), plot_colors):
          idx = np.where(y == i)
          plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                      cmap=plt.cm.Paired)
  
      plt.axis("tight")
  
  plt.suptitle("Decision surface of a decision tree using paired features",
      position=(0.5, 0.035))        
  handles, labels = plt.gca().get_legend_handles_labels()
  plt.figlegend(handles=handles, labels=labels, loc='upper left')
  plt.tight_layout(rect=[0, 0.03, 1, 0.8])
  plt.show()
  
new_contents: """
  ================================================================
  Plot the decision surface of a decision tree on the iris dataset
  ================================================================
  
  Plot the decision surface of a decision tree trained on pairs
  of features of the iris dataset.
  
  See :ref:`decision tree <tree>` for more information on the estimator.
  
  For each pair of iris features, the decision tree learns decision
  boundaries made of combinations of simple thresholding rules inferred from
  the training samples.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.datasets import load_iris
  from sklearn.tree import DecisionTreeClassifier
  
  # Parameters
  n_classes = 3
  plot_colors = "bry"
  plot_step = 0.02
  
  # Load data
  iris = load_iris()
  
  for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
                                  [1, 2], [1, 3], [2, 3]]):
      # We only take the two corresponding features
      X = iris.data[:, pair]
      y = iris.target
  
      # Train
      clf = DecisionTreeClassifier().fit(X, y)
  
      # Plot the decision boundary
      plt.subplot(2, 3, pairidx + 1)
  
      x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
      y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
      xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                           np.arange(y_min, y_max, plot_step))
  
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
  
      plt.xlabel(iris.feature_names[pair[0]])
      plt.ylabel(iris.feature_names[pair[1]])
      plt.axis("tight")
  
      # Plot the training points
      for i, color in zip(range(n_classes), plot_colors):
          idx = np.where(y == i)
          plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                      cmap=plt.cm.Paired)
  
      plt.axis("tight")
  
  plt.suptitle("Decision surface of a decision tree using paired features")
  plt.legend()
  plt.show()
  
subject: FIX restore previous title layout in examples/tree/plot_iris.py