So far in this book, we have used supervised learning algorithms to spot anomalous samples. This chapter offered additional solutions when no labels are provided. The solutions explained here stem from different fields of machine learning, such as statistical learning, nearest-neighbor, and tree-based ensembles. Each one of the three tools explained here can excel, but also have disadvantages. We also learned that evaluating machine learning algorithms when no labels are provided is tricky.
This chapter will deal with unlabeled data. In the previous chapter, we learned how to cluster data, and then we learned how to detect the outliers in it here. We still have one more unsupervised learning topic to discuss in this book, though. In the next chapter, we will cover an important topic relating to e-commerce—recommendation engines. Since it is the last chapter of this book, I'd alsolike to go through the possible approaches to machine learning model...
