old_contents: """
  ============================================================
  Parameter estimation using grid search with cross-validation
  ============================================================
  
  This examples shows how a classifier is optimized by cross-validation,
  which is done using the :class:`sklearn.model_selection.GridSearchCV` object
  on a development set that comprises only half of the available labeled data.
  
  The performance of the selected hyper-parameters and trained model is
  then measured on a dedicated evaluation set that was not used during
  the model selection step.
  
  More details on tools available for model selection can be found in the
  sections on :ref:`cross_validation` and :ref:`grid_search`.
  
  """
  
  from __future__ import print_function
  
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import GridSearchCV
  from sklearn.metrics import classification_report
  from sklearn.svm import SVC
  
  print(__doc__)
  
  # Loading the Digits dataset
  digits = datasets.load_digits()
  
  # To apply an classifier on this data, we need to flatten the image, to
  # turn the data in a (samples, feature) matrix:
  n_samples = len(digits.images)
  X = digits.images.reshape((n_samples, -1))
  y = digits.target
  
  # Split the dataset in two equal parts
  X_train, X_test, y_train, y_test = train_test_split(
      X, y, test_size=0.5, random_state=0)
  
  # Set the parameters by cross-validation
  tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                       'C': [1, 10, 100, 1000]},
                      {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
  
  scores = ['precision', 'recall']
  
  for score in scores:
      print("# Tuning hyper-parameters for %s" % score)
      print()
  
      clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5,
                         scoring='%s_macro' % score)
      clf.fit(X_train, y_train)
  
      print("Best parameters set found on development set:")
      print()
      print(clf.best_params_)
      print()
      print("Grid scores on development set:")
      print()
      means = clf.cv_results_['mean_test_score']
      stds = clf.cv_results_['std_test_score']
      for mean, std, params in zip(means, stds, clf.cv_results_['params']):
          print("%0.3f (+/-%0.03f) for %r"
                % (mean, std * 2, params))
      print()
  
      print("Detailed classification report:")
      print()
      print("The model is trained on the full development set.")
      print("The scores are computed on the full evaluation set.")
      print()
      y_true, y_pred = y_test, clf.predict(X_test)
      print(classification_report(y_true, y_pred))
      print()
  
  # Note the problem is too easy: the hyperparameter plateau is too flat and the
  # output model is the same for precision and recall with ties in quality.
  
new_contents: """
  ============================================================
  Parameter estimation using grid search with cross-validation
  ============================================================
  
  This examples shows how a classifier is optimized by cross-validation,
  which is done using the :class:`sklearn.model_selection.GridSearchCV` object
  on a development set that comprises only half of the available labeled data.
  
  The performance of the selected hyper-parameters and trained model is
  then measured on a dedicated evaluation set that was not used during
  the model selection step.
  
  More details on tools available for model selection can be found in the
  sections on :ref:`cross_validation` and :ref:`grid_search`.
  
  """
  
  from __future__ import print_function
  
  from sklearn import datasets
  from sklearn.model_selection import train_test_split
  from sklearn.model_selection import GridSearchCV
  from sklearn.metrics import classification_report
  from sklearn.svm import SVC
  
  print(__doc__)
  
  # Loading the Digits dataset
  digits = datasets.load_digits()
  
  # To apply an classifier on this data, we need to flatten the image, to
  # turn the data in a (samples, feature) matrix:
  n_samples = len(digits.images)
  X = digits.images.reshape((n_samples, -1))
  y = digits.target
  
  # Split the dataset in two equal parts
  X_train, X_test, y_train, y_test = train_test_split(
      X, y, test_size=0.5, random_state=0)
  
  # Set the parameters by cross-validation
  tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                       'C': [1, 10, 100, 1000]},
                      {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
  
  scores = ['precision', 'recall']
  
  for score in scores:
      print("# Tuning hyper-parameters for %s" % score)
      print()
  
      clf = GridSearchCV(SVC(), tuned_parameters, cv=5,
                         scoring='%s_macro' % score)
      clf.fit(X_train, y_train)
  
      print("Best parameters set found on development set:")
      print()
      print(clf.best_params_)
      print()
      print("Grid scores on development set:")
      print()
      means = clf.cv_results_['mean_test_score']
      stds = clf.cv_results_['std_test_score']
      for mean, std, params in zip(means, stds, clf.cv_results_['params']):
          print("%0.3f (+/-%0.03f) for %r"
                % (mean, std * 2, params))
      print()
  
      print("Detailed classification report:")
      print()
      print("The model is trained on the full development set.")
      print("The scores are computed on the full evaluation set.")
      print()
      y_true, y_pred = y_test, clf.predict(X_test)
      print(classification_report(y_true, y_pred))
      print()
  
  # Note the problem is too easy: the hyperparameter plateau is too flat and the
  # output model is the same for precision and recall with ties in quality.
  
subject: Remove impact-less (and confusing) 'C=1' parameter. (#8731)