Introduction to Machine Learning with R

Chapter 2. Supervised and Unsupervised Machine Learning

In the universe of machine learning algorithms, there are two major types: supervised and unsupervised. Supervised learning models are those in which a machine learning model is scored and tuned against some sort of known quantity. The majority of machine learning algorithms are supervised learners. Unsupervised learning models are those in which the machine learning model derives patterns and information from data while determining the known quantity tuning parameter itself. These are more rare in practice, but are useful in their own right and can help guide our thinking on where to explore the data for further analysis.

An example of supervised learning might be something like this: we have a model we’ve built that says “any business that sells less than 10 units is a poor performer, and more than 10 units is a good performer.” We then have a set of data we want to test against that statement. Suppose that our data includes a store that sells eight units. That is less than 10, so according to our model definition, it is classified as a poor performer. In this situation, we have a model that ingests data in which we’re interested and gives us an output as decided by the conditions in the model.

In contrast, an unsupervised learning model might be something like this: we have a bunch of data and we want to know how to separate it into meaningful groups. We could have a bunch of data from a survey about people’s height and ...