old_contents: """
  =================================================
  SVM-Anova: SVM with univariate feature selection
  =================================================
  
  This example shows how to perform univariate feature before running a SVC
  (support vector classifier) to improve the classification scores.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import svm, datasets, feature_selection
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  
  ###############################################################################
  # Import some data to play with
  digits = datasets.load_digits()
  y = digits.target
  # Throw away data, to be in the curse of dimension settings
  y = y[:200]
  X = digits.data[:200]
  n_samples = len(y)
  X = X.reshape((n_samples, -1))
  # add 200 non-informative features
  X = np.hstack((X, 2 * np.random.random((n_samples, 200))))
  
  ###############################################################################
  # Create a feature-selection transform and an instance of SVM that we
  # combine together to have an full-blown estimator
  
  transform = feature_selection.SelectPercentile(feature_selection.f_classif)
  
  clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))])
  
  ###############################################################################
  # Plot the cross-validation score as a function of percentile of features
  score_means = list()
  score_stds = list()
  percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
  
  for percentile in percentiles:
      clf.set_params(anova__percentile=percentile)
      # Compute cross-validation score using 1 CPU
      this_scores = cross_val_score(clf, X, y, n_jobs=1)
      score_means.append(this_scores.mean())
      score_stds.append(this_scores.std())
  
  plt.errorbar(percentiles, score_means, np.array(score_stds))
  
  plt.title(
      'Performance of the SVM-Anova varying the percentile of features selected')
  plt.xlabel('Percentile')
  plt.ylabel('Prediction rate')
  
  plt.axis('tight')
  plt.show()
  
new_contents: """
  =================================================
  SVM-Anova: SVM with univariate feature selection
  =================================================
  
  This example shows how to perform univariate feature selection before running a
  SVC (support vector classifier) to improve the classification scores.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import svm, datasets, feature_selection
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  
  ###############################################################################
  # Import some data to play with
  digits = datasets.load_digits()
  y = digits.target
  # Throw away data, to be in the curse of dimension settings
  y = y[:200]
  X = digits.data[:200]
  n_samples = len(y)
  X = X.reshape((n_samples, -1))
  # add 200 non-informative features
  X = np.hstack((X, 2 * np.random.random((n_samples, 200))))
  
  ###############################################################################
  # Create a feature-selection transform and an instance of SVM that we
  # combine together to have an full-blown estimator
  
  transform = feature_selection.SelectPercentile(feature_selection.f_classif)
  
  clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))])
  
  ###############################################################################
  # Plot the cross-validation score as a function of percentile of features
  score_means = list()
  score_stds = list()
  percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
  
  for percentile in percentiles:
      clf.set_params(anova__percentile=percentile)
      # Compute cross-validation score using 1 CPU
      this_scores = cross_val_score(clf, X, y, n_jobs=1)
      score_means.append(this_scores.mean())
      score_stds.append(this_scores.std())
  
  plt.errorbar(percentiles, score_means, np.array(score_stds))
  
  plt.title(
      'Performance of the SVM-Anova varying the percentile of features selected')
  plt.xlabel('Percentile')
  plt.ylabel('Prediction rate')
  
  plt.axis('tight')
  plt.show()
  
subject: Fix omitted word in SVM-Anova example