Hands-On Machine Learning for Algorithmic Trading

Multinomial Naive Bayes

We create a document-term matrix with 934 tokens as follows:

vectorizer = CountVectorizer(min_df=.001, max_df=.8, stop_words='english')train_dtm = vectorizer.fit_transform(train.text)<1566668x934 sparse matrix of type '<class 'numpy.int64'>'   with 6332930 stored elements in Compressed Sparse Row format>

We then train the MultinomialNB classifier as before and predict the test set:

nb = MultinomialNB()nb.fit(train_dtm, train.polarity)predicted_polarity = nb.predict(test_dtm)

The result is over 77.5% accuracy:

accuracy_score(test.polarity, y_pred_class)0.7768361581920904