Practicing and exploring

Test your knowledge and understanding by answering some questions, get some hands-on practice, and explore this chapter's topics with deeper research.

Exercise 19.1 – Test your knowledge

Answer the following questions:

1. What are the four main steps of the machine learning lifecycle?
2. What are the three sub-steps of the modeling step?
3. Why do models need to be retrained after deployment?
4. Why must you split your dataset into a training dataset and a testing dataset?
5. What are some of the differences between clustering and classification machine learning tasks?
6. What class must you instantiate to perform any machine learning task?
7. What is the difference between a label and a feature?
8. What does IDataView represent?
9. What does the count parameter in the [KeyType(count: 10)] attribute represent?

10. What does the score represent with matrix factorization?

Exercise 19.2 – Practice with samples...