This chapter begins by analyzing linear classification problems, with a particular focus on logistic regression (despite its name, it's a classification algorithm) and the stochastic gradient descent (SGD) approach. Even if these strategies appear too simple, they're still the main choices in many classification tasks.
Speaking of which, it's useful to remember a very important philosophical principle: Occam's razor.
In our context, it states that the first choice must always be the simplest and only if it doesn't fit, it's necessary to move on to more complex models. In the second part of the chapter, we're going to discuss some common metrics that are helpful when evaluating a classification task. They are not limited to linear models, so we use them when talking about different strategies as well.
In particular...
