Supervised learning is all about making predictions. We utilize features of the data and use them to make informative predictions about the response of the data. If we aren’t making predictions by exploring structure, we are attempting to extract structure from our data. We generally do so by applying mathematical transformations to numerical matrix representations of data or iterative procedures to obtain new sets of features.

This concept can be a bit more difficult to grasp than supervised learning, and so I will present a motivating example to help elucidate how this all works.