Principles of Data Science

Unsupervised learning

It's time to see some examples of unsupervised learning, given that we spend a majority of this book on supervised learning models.

When to use unsupervised learning

There are many times when unsupervised learning can be appropriate. Some very common examples include the following:

• When 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. ...