We have covered an unsupervised learning algorithm: k-means clustering. It is time we move on to the supervised counterparts. We are going to discuss a machine learning algorithm that goes by the name of k-nearest neighbors classifier, often abbreviated as the kNN classifier. Although the names of both (k-means and kNN) sound similar, they are, in fact, somewhat different in their working, the most glaring difference being the fact that k-means clustering is an unsupervised technique used to divide the data points into meaningful clusters, while the kNN algorithm is a classifier that associates a class label with each data point.
As always, let's use an example to motivate the main concepts behind the kNN classification algorithm. In the previous example, we had information about the marks of every student in a couple of subjects. Based on this information, our goal was to divide them into some meaningful groups so that each group can then be...
