title: Does GridSearchCV store all the scores for all parameter combinations?
body: <p>The GridSearchCV use 'scoring' to select best estimator. After train the GridSearchCV, I would like to see the score for each combination. Does GridSearchCV store all scores for each parameter combinations? If it does how to get the scores? Thanks.</p>
  
  <p>Here is an example code that I used in another post. </p>
  
  <pre><code>from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.grid_search import GridSearchCV
  from sklearn.pipeline import Pipeline
  from sklearn.naive_bayes import MultinomialNB
  
  X_train = ['qwe rtyuiop', 'asd fghj kl', 'zx cv bnm', 'qw erty ui op', 'as df ghj kl', 'zxc vb nm', 'qwe rt yu iop', 'asdfg hj kl', 'zx cvb nm',
            'qwe rt yui op', 'asd fghj kl', 'zx cvb nm', 'qwer tyui op', 'asd fg hjk l', 'zx cv b nm', 'qw ert yu iop', 'as df gh jkl', 'zx cvb nm',
             'qwe rty uiop', 'asd fghj kl', 'zx cvbnm', 'qw erty ui op', 'as df ghj kl', 'zxc vb nm', 'qwe rtyu iop', 'as dfg hj kl', 'zx cvb nm',
            'qwe rt yui op', 'asd fg hj kl', 'zx cvb nm', 'qwer tyuiop', 'asd fghjk l', 'zx cv b nm', 'qw ert yu iop', 'as df gh jkl', 'zx cvb nm']    
  
  y_train = ['1', '2', '3', '1', '1', '3', '1', '2', '3',
            '1', '2', '3', '1', '4', '1', '2', '2', '4', 
            '1', '2', '3', '1', '1', '3', '1', '2', '3',
            '1', '2', '3', '1', '4', '1', '2', '2', '4']    
  
  
  parameters = {  
                  'clf__alpha': (1e-1, 1e-2),
                   'vect__ngram_range': [(1,2),(1,3)],
                   'vect__max_df': (0.9, 0.98)
              }
  
  text_clf_Pipline_MultinomialNB = Pipeline([('vect', CountVectorizer()),
                                             ('tfidf', TfidfTransformer()),
                                             ('clf', MultinomialNB()),                     
                                            ])
  gs_clf = GridSearchCV(text_clf_Pipline_MultinomialNB, parameters, n_jobs=-1)   
  
  gs_classifier = gs_clf.fit(X_train, y_train)
  </code></pre>