# Import the necessary modules
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
# Instantiate a Multinomial Naive Bayes classifier: nb_classifier
nb_classifier = MultinomialNB()
# Fit the classifier to the training data
nb_classifier.fit(count_train, y_train)
# Create the predicted tags: pred
pred = nb_classifier.predict(count_test)
# Calculate the accuracy score: score
score = metrics.accuracy_score(y_test, pred)
print(score)
# Calculate the confusion matrix: cm
cm = metrics.confusion_matrix(y_test, pred, labels=['FAKE', 'REAL'])
print(cm)