In this chapter, we learned about classification using k-NN. Unlike many classification algorithms, k-nearest neighbors does not do any learning. It simply stores the training data verbatim. Unlabeled test examples are then matched to the most similar records in the training set using a distance function, and the unlabeled example is assigned the label of its neighbors.

In spite of the fact that k-NN is a very simple algorithm, it is capable of tackling extremely complex tasks such as the identification of cancerous masses. In a few simple lines of R code, we were able to correctly identify whether a mass was malignant or benign 98 percent of the time.

In the next chapter, we will examine a classification method that uses probability to estimate the likelihood that an observation falls into certain categories. It will be interesting to compare how this approach differs from k-NN. Later on, in Chapter 9, Finding Groups of Data – Clustering with k-means, we will learn about a close relative to k-NN, which uses distance measures for a completely different learning task.