Chapter 11: Classification Analysis

When we speak about the field of machine learning and specifically the types of machine learning algorithms, we tend to invoke a taxonomy of three different classes of algorithms: supervised learning, unsupervised learning, and reinforcement learning. The third one falls outside of the scope of both this book and the current features available in the Elastic Stack, while the second one has been our topic of investigation throughout the chapters on anomaly detection, as well as the previous chapter on outlier detection. In this chapter, we will finally start dipping our toes into the world of supervised learning. The Elastic Stack provides two flavors of supervised learning: classification and regression. This chapter will be dedicated to understanding the former, while the subsequent chapter will tackle the latter.

The goal of supervised learning is to take a labeled dataset and extract the patterns from it, encode the knowledge obtained from...