title: How to access Scikit Learn nested cross-validation scores
body: <p>I'm using python and I would like to use nested cross-validation with scikit learn. I have found a very good <a href="http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html" rel="nofollow noreferrer">example</a>:</p>
  
  <pre><code>NUM_TRIALS = 30
  non_nested_scores = np.zeros(NUM_TRIALS)
  nested_scores = np.zeros(NUM_TRIALS)
  # Choose cross-validation techniques for the inner and outer loops,
  # independently of the dataset.
  # E.g "LabelKFold", "LeaveOneOut", "LeaveOneLabelOut", etc.
  inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
  outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)
  
  # Non_nested parameter search and scoring
  clf = GridSearchCV(estimator=svr, param_grid=p_grid, cv=inner_cv)
  clf.fit(X_iris, y_iris)
  non_nested_scores[i] = clf.best_score_
  
  # Nested CV with parameter optimization
  nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)
  nested_scores[i] = nested_score.mean()
  </code></pre>
  
  <p>How can the best set of parameters as well as all set of parameters (with their corresponding score) from the nested cross-validation be accessed?</p>