Questions

Here are 10 questions that will help to reinforce all of the learning material presented in this chapter:

1.  Is the spam classification task a binary classification task?
2. What was the significance of the hashing trick in the spam classification task?
3. What is hashing collision and how is it minimized?
4. What do we mean by inverse document frequency?
5. What are stop words and why do they matter?
6. What is the role played by the Naive Bayes algorithm in spam classification?
7. How do you use the HashingTF class in Spark to implement the hashing trick in your spam classification process?
8. What is meant by the vectorization of features?
9. Is there a better algorithm that you can think of to implement the spam classification process? 
10. What are the benefits of spam filtering, and why do they matter in business terms?