content: """
  ===================================================================
  Decision Tree Regression
  ===================================================================
  
  A 1D regression with decision tree.
  
  The :ref:`decision trees <tree>` is
  used to fit a sine curve with addition noisy observation. As a result, it
  learns local linear regressions approximating the sine curve.
  
  We can see that if the maximum depth of the tree (controlled by the
  `max_depth` parameter) is set too high, the decision trees learn too fine
  details of the training data and learn from the noise, i.e. they overfit.
  """
  print(__doc__)
  
  # Import the necessary modules and libraries
  import numpy as np
  from sklearn.tree import DecisionTreeRegressor
  import matplotlib.pyplot as plt
  
  # Create a random dataset
  rng = np.random.RandomState(1)
  X = np.sort(5 * rng.rand(80, 1), axis=0)
  y = np.sin(X).ravel()
  y[::5] += 3 * (0.5 - rng.rand(16))
  
  # Fit regression model
  regr_1 = DecisionTreeRegressor(max_depth=2)
  regr_2 = DecisionTreeRegressor(max_depth=5)
  regr_1.fit(X, y)
  regr_2.fit(X, y)
  
  # Predict
  X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
  y_1 = regr_1.predict(X_test)
  y_2 = regr_2.predict(X_test)
  
  # Plot the results
  plt.figure()
  plt.scatter(X, y, c="darkorange", label="data")
  plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
  plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
  plt.xlabel("data")
  plt.ylabel("target")
  plt.title("Decision Tree Regression")
  plt.legend()
  plt.show()
  