title: Pass estimator to custom score function via sklearn.metrics.make_scorer
body: <p>I'd like to make a custom scoring function involving classification probabilities as follows:</p>
  
  <pre><code>def custom_score(y_true, y_pred_proba):
      error = ...
      return error
  
  my_scorer = make_scorer(custom_score, needs_proba=True)
  
  gs = GridSearchCV(estimator=KNeighborsClassifier(),
                    param_grid=[{'n_neighbors': [6]}],
                    cv=5,
                    scoring=my_scorer)
  </code></pre>
  
  <p>Is there any way to pass the estimator, as fit by GridSearch with the given data and parameters, to my custom scoring function? Then I could interpret the probabilities using <code>estimator.classes_</code></p>
  
  <p>For example:</p>
  
  <pre><code>def custom_score(y_true, y_pred_proba, clf):
      class_labels = clf.classes_
      error = ...
      return error
  </code></pre>