Database Reference
In-Depth Information
Summary
In this chapter, we explored a new class of model that learns structure from unlabeled
data—unsupervised learning. We worked through required input data, feature extraction,
and saw how to use the output of one model (a recommendation model in our example) as
the input to another model (our K-means clustering model). Finally, we evaluated the per-
formance of the clustering model, both using manual interpretation of the cluster assign-
ments and using mathematical performance metrics.
In the next chapter, we will cover another type of unsupervised learning used to reduce our
data down to its most important features or components—dimensionality reduction models.
