Training and testing the "fake news" model with TfidfVectorizer
Now that you have evaluated the model using the CountVectorizer, you'll do the same using the TfidfVectorizer with a Naive Bayes model.
The training and test sets have been created, and tfidf_vectorizer, tfidf_train, and tfidf_test have been computed. Additionally, MultinomialNB and metrics have been imported from, respectively, sklearn.naive_bayes and sklearn.
Questo esercizio fa parte del corso
Introduction to Natural Language Processing in Python
Istruzioni dell'esercizio
- Instantiate a
MultinomialNBclassifier callednb_classifier. - Fit the classifier to the training data.
- Compute the predicted tags for the test data.
- Calculate and print the accuracy score of the classifier.
- Compute the confusion matrix. As in the previous exercise, specify the keyword argument
labels=['FAKE', 'REAL']so that the resulting confusion matrix is easier to read.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Create a Multinomial Naive Bayes classifier: nb_classifier
nb_classifier = ____
# Fit the classifier to the training data
____
# Create the predicted tags: pred
pred = ____
# Calculate the accuracy score: score
score = ____
print(score)
# Calculate the confusion matrix: cm
cm = ____
print(cm)