One common type of supervised learning is classification. Classification is the act of TRAINING an algorithm to predict a dependent VARIABLE that is categorical (belonging to a discrete, finite set of values). The most common case is binary classification, where our resulting model will make a prediction that a given item belongs to one of two groups.

The canonical example is CLASSIFYING email spam. USING a set of historical emails that are organized into groups of spam emails and not spam emails, we train an algorithm to analyze the words in, and any number of properties of, the historical emails and make PREDICTIONS about them. Once we are satisfied with the algorithm’s performance, we use that model to make predictions about future emails the model has never seen before.

When we classify items into more than just two categories, we call this multiclass classification. For example, we may have four different categories of an email (as opposed to the two categories in the previous paragraph): spam, personal, work-related, and other.