title: Get a classification report stating the class wise precision and recall for multinomial Naive Bayes using 10 fold cross validation
body: <p>I have the following piece of code which uses an NB classifier for a multi class classification problem. The function preforms cross validation by storing the accuracies and printing the average later. What I instead want is a classification report specifying class wise precision and recall, instead of a mean accuracy score in the end.</p>
  
  <pre><code>   import random
     from sklearn import cross_validation
     from sklearn.naive_bayes import MultinomialNB
  
     def multinomial_nb_with_cv(x_train, y_train):
          random.shuffle(X)
          kf = cross_validation.KFold(len(X), n_folds=10)
          acc = []
          for train_index, test_index in kf:
              y_true = y_train[test_index]
              clf = MultinomialNB().fit(x_train[train_index],         
              y_train[train_index])
              y_pred = clf.predict(x_train[test_index])
              acc.append(accuracy_score(y_true, y_pred))
  </code></pre>
  
  <p>If I do not perform cross validation all I have to do is:</p>
  
  <pre><code>    from sklearn.metrics import classification_report
      from sklearn.naive_bayes import MultinomialNB
  
      def multinomial_nb(x_train, y_train, x_test, y_test):
          clf = MultinomialNB().fit(x_train, y_train)
          y_pred = clf.predict(x_test)
          y_true = y_test
          print classification_report(y_true, y_pred)
  </code></pre>
  
  <p>And it gives me a report like this:</p>
  
  <pre><code>        precision    recall  f1-score   support
  
        0       0.50      0.24      0.33       221
        1       0.00      0.00      0.00        18
        2       0.00      0.00      0.00        27
        3       0.00      0.00      0.00        28
        4       0.00      0.00      0.00        32
        5       0.04      0.02      0.02        57
        6       0.00      0.00      0.00        26
        7       0.00      0.00      0.00        25
        8       0.00      0.00      0.00        43
        9       0.00      0.00      0.00        99
       10       0.63      0.98      0.76       716
  
      avg / total       0.44      0.59      0.48      1292
  </code></pre>
  
  <p>How can I get a similar report even in the case of cross validation?</p>