KNN is a simple model for regression and classification tasks. It is so simple that its name describes most of its learning algorithm. The titular neighbors are representations of training instances in a metric space. A metric space is a feature space in which the distances between all members of a set are defined. In the previous chapter's pizza problem, our training instances were represented in a metric space because the distances between all the pizza diameters was defined. These neighbors are used to estimate the value of the response variable for a test instance. The hyperparameter k specifies how many neighbors can be used in the estimation. A hyperparameter is a parameter that controls how the algorithm learns; hyperparameters are not estimated from the training data and are sometimes set manually. Finally, the k neighbors that are selected are those that are nearest to the test instance, as measured by some distance function.
For classification tasks, a set of...