What is unsupervised learning?

So far, we have focused on models and techniques that broadly fall under the category of supervised learning. Supervised learning is supervised because the task is for the machine to learn the relationship between a set of variables or features and one or more outcomes. For example, in Chapter 4, Training Deep Prediction Models, we wanted to predict whether someone would visit a store in the next 14 days. In this chapter, we will delve into methods of unsupervised learning. In contrast with supervised learning, where there is an outcome variable(s) or labeled data is being used, unsupervised learning does not use any outcomes or labeled data. Unsupervised learning uses only input features for learning. A common example of unsupervised learning is cluster analysis, such as k-means clustering, where the machine learns hidden or latent clusters in the data to minimize a criterion (for example, the smallest variance within a cluster).

Another unsupervised learning...