This chapter covers
- Working on an outlier detection project in production
- The types of problems we may work with
- Where outlier detectors are actually the best option
- Collecting and preparing data as well as fitting the models
- Evaluating and combining models
We now have a good sense of how outlier detection works generally and how some specific algorithms to identify anomalies work, including statistical and machine learning-based methods. There are, though, a number of steps involved with effectively executing an outlier detection project, which, now that we have a good foundation, we should look at.
In this chapter, we’ll go through the main steps typically involved in outlier detection projects, though they will, of course, vary. If you’re familiar with other areas of machine learning, such as prediction, the steps with outlier detection will be very similar. Each of these steps is important, and each has some subtle points, often a little different than the corresponding steps for prediction projects.