It's time to look at some examples of unsupervised learning, given that we spend the majority of this book on supervised learning models.
There are many times when unsupervised learning can be appropriate. Some very common examples include the following:
- There is no clear response variable. There is nothing that we are explicitly trying to predict or correlate to other variables.
- To extract structure from data where no apparent structure/patterns exist (can be a supervised learning problem).
- When an unsupervised concept called feature extraction is used. Feature extraction is the process of creating new features from existing ones. These new features can be even stronger than the original features.
The first tends to be the most common reason that data scientists choose to use unsupervised learning. This case arises frequently when we are working with data and we are not explicitly trying to predict any of the columns, and we merely...