In Section 3.1, we introduced linear regression, working through implementations from scratch in Section 3.2 and again using high-level APIs of a deep learning framework in Section 3.3 to do the heavy lifting.

Regression is the hammer we reach for when we want to answer how much? or how many? questions. If you want to predict the number of dollars (price) at which a house will be sold, or the number of wins a baseball team might have, or the number of days that a patient will remain hospitalized before being discharged, then you are probably looking for a regression model.

In practice, we are more often interested in classification: asking not “how much” but “which one”:

• Does this email belong in the spam folder or the inbox?
• Is this customer more likely to sign up or not to sign up for a subscription service?
• Does this image depict a donkey, a dog, a cat, or a rooster?
• Which movie is Aston most likely to watch next?

Colloquially, machine learning practitioners overload the word classification to describe two subtly different problems: (i) those where we are interested only in hard assignments of examples to categories (classes); and (ii) those where we wish to make soft assignments, i.e., to assess the probability that each category applies. The distinction tends to get blurred, in part, because often, even when we only care about hard assignments, we still use models that make soft assignments.