Summary

In this chapter, we have taken a deep dive into supervised learning. We have examined what supervised learning means, what role is played by training data in constructing the model, what it means to train a supervised learning model, what features are and how they should be engineered to obtain optimal performance, as well as how a model is evaluated and what various model performance measures mean.

After learning about the basics of supervised learning in general, we took a closer look at classification and examined how one can create and run classification jobs in the Elastic Stack as well as how one can evaluate the trained models that are produced by these jobs. In addition to looking at basic concepts such as confusion matrices, we also examined situations where it is good to be skeptical about results that seem to be too good to be true and the potential underlying reasons why classification results can sometimes appear perfect and why this does not necessarily mean...