content: """
  ===================================================
  Recursive feature elimination with cross-validation
  ===================================================
  
  A recursive feature elimination example with automatic tuning of the
  number of features selected with cross-validation.
  """
  print(__doc__)
  
  from sklearn.svm import SVC
  from sklearn.cross_validation import StratifiedKFold
  from sklearn.feature_selection import RFECV
  from sklearn.datasets import make_classification
  
  # Build a classification task using 3 informative features
  X, y = make_classification(n_samples=1000, n_features=25, n_informative=3,
                             n_redundant=2, n_repeated=0, n_classes=8,
                             n_clusters_per_class=1, random_state=0)
  
  # Create the RFE object and compute a cross-validated score.
  svc = SVC(kernel="linear")
  # The "accuracy" scoring is proportional to the number of correct
  # classifications
  rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, 2),
                scoring='accuracy')
  rfecv.fit(X, y)
  
  print("Optimal number of features : %d" % rfecv.n_features_)
  
  # Plot number of features VS. cross-validation scores
  import matplotlib.pyplot as plt
  plt.figure()
  plt.xlabel("Number of features selected")
  plt.ylabel("Cross validation score (nb of correct classifications)")
  plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
  plt.show()
  