content: """
  ======================================
  Decision Tree Regression with AdaBoost
  ======================================
  
  A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
  sinusoidal dataset with a small amount of Gaussian noise.
  299 boosts (300 decision trees) is compared with a single decision tree
  regressor. As the number of boosts is increased the regressor can fit more
  detail.
  
  .. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
  
  """
  print(__doc__)
  
  # Author: Noel Dawe <noel.dawe@gmail.com>
  #
  # License: BSD 3 clause
  
  # importing necessary libraries
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.ensemble import AdaBoostRegressor
  
  # Create the dataset
  rng = np.random.RandomState(1)
  X = np.linspace(0, 6, 100)[:, np.newaxis]
  y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
  
  # Fit regression model
  regr_1 = DecisionTreeRegressor(max_depth=4)
  
  regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
                            n_estimators=300, random_state=rng)
  
  regr_1.fit(X, y)
  regr_2.fit(X, y)
  
  # Predict
  y_1 = regr_1.predict(X)
  y_2 = regr_2.predict(X)
  
  # Plot the results
  plt.figure()
  plt.scatter(X, y, c="k", label="training samples")
  plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
  plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
  plt.xlabel("data")
  plt.ylabel("target")
  plt.title("Boosted Decision Tree Regression")
  plt.legend()
  plt.show()
  