title: Trying to train a MultinomialNB classifier with Bag Of Word technique
body: <p>Hi every body I'm trying to train a Multinomial DB with BOW techinque.
  Here the code of BOW:</p>
  <pre><code>vectorizer = CountVectorizer()
  X_train_bow = vectorizer.fit_transform(train_X)
  
  #BOW model (Test Set) 
  vectorizer = CountVectorizer()
  X_test_bow = vectorizer.fit_transform(test_X)
  print(X_test_bow)
  </code></pre>
  <p>Here my classifier code:</p>
  <pre><code>#MULTINOMIAL NAIVE BAYES CLASSIFIER + BOW
  naive_bayes_classifier = MultinomialNB()
  naive_bayes_classifier.fit(X_train_bow, train_y)
  y_pred = naive_bayes_classifier.predict(X_test_bow)
  print(metrics.classification_report(test_y, y_pred, target_names=['Bad', 'Neutral','Good']))
  </code></pre>
  <p>I obtain this error:</p>
  <pre><code>ValueError: X has 9601 features, but MultinomialNB is expecting 10972 features as input.
  </code></pre>
  <p>What am I doing wrong?
  Thanks in advance.</p>