title: Get precison model through GridSearchCV for recall optimization
body: <p>Given a machine learning model  RBF SVC called 'm', I performed a gridSearchCV on gamma value, to optimize recall.
  I'm looking to answer to this:
  "The grid search should find the model that best optimizes for recall. How much better is the recall of this model than the precision?"</p>
  
  <p>So I did the gridSearchCV: </p>
  
  <pre><code>grid_values = {'gamma': [0.001, 0.01, 0.05, 0.1, 1, 10, 100]}
  grid_m_re = GridSearchCV(m, param_grid = grid_values, scoring = 'recall')
  grid_m_re.fit(X_train, y_train)
  y_decision_fn_scores_re = grid_m_re.decision_function(X_test) 
  
  print('Grid best parameter (max. recall): ', grid_m_re.best_params_)
  print('Grid best score (recall): ', grid_m_re.best_score_)
  </code></pre>
  
  <p>This tell me the best model is for gamma=0.001 and it has a recall score of 1.</p>
  
  <p>I'm wondering how to get the precision for this model to get the trade of of this model, cause the GridSearchCV only has attribute to get what it was optimize for.(<code>[Doc sklearn.GridSearchCV][1]</code>)</p>