title: AttributeError: 'GaussianNB' object has no attribute 'accuracy_score'
body: <p>While executing the following I got the error "AttributeError: 'GaussianNB' object has no attribute 'accuracy_score' "</p>
  
  <pre><code>    import sys
      from time import time
      sys.path.append("../tools/")
      from email_preprocess import preprocess
      features_train, features_test, labels_train, labels_test = preprocess()
      from sklearn.naive_bayes import GaussianNB
      clf = GaussianNB()
      clf.fit(features_train, labels_train)
      pred=clf.predict(features_test)
      from sklearn.metrics import accuracy_score
      print clf.accuracy_score(pred, labels_test)
  </code></pre>
  
  <p>and the email_preprocess.py is as follows</p>
  
  <pre><code>   import pickle
     import cPickle
     import numpy
     from sklearn import cross_validation
     from sklearn.feature_extraction.text import TfidfVectorizer
     from sklearn.feature_selection import SelectPercentile, f_classif
     def preprocess(words_file = "../tools/word_data.pkl", 
     authors_file="../tools/email_authors.pkl"):
         authors_file_handler = open(authors_file, "r")
         authors = pickle.load(authors_file_handler)
         authors_file_handler.close()
         words_file_handler = open(words_file, "r")
         word_data = cPickle.load(words_file_handler)
         words_file_handler.close()
         features_train, features_test, labels_train, labels_test = 
         cross_validation.train_test_split(word_data, authors, 
         test_size=0.5,random_state=42)
         vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                               stop_words='english')
         features_train_transformed = vectorizer.fit_transform(features_train)
         features_test_transformed  = vectorizer.transform(features_test)
         selector = SelectPercentile(f_classif, percentile=10)
         selector.fit(features_train_transformed, labels_train)
         features_train_transformed = 
         selector.transform(features_train_transformed).toarray()
         features_test_transformed  = 
         selector.transform(features_test_transformed).toarray()
         print "no. of Chris training emails:", sum(labels_train)
         print "no. of Sara training emails:", len(labels_train)-
         sum(labels_train)
         return features_train_transformed, features_test_transformed, 
         labels_train, labels_test
  </code></pre>
  
  <p>Can anyone help me with this?</p>