For regression tasks where the goal is to predict a numerical output, such as price or temperature, we've seen that linear regression can potentially be a good starting point. It is simple to train and easy to interpret even though, as a model, it makes strict assumptions about the data and the underlying target function. Before studying more advanced techniques to tackle regression problems, we'll introduce logistic regression. Despite its somewhat misleading name, this is actually our first model for performing classification. As we learned in Chapter 1, Gearing Up for Predictive Modeling, in classification problems, our output is qualitative and is thus comprised of a finite set of values, which we call classes. We'll begin by thinking about the binary classification scenario, where we are trying to distinguish between two classes, which we'll arbitrarily label as 0 and 1, and later on, we'll extend this to distinguishing between multiple classes.