This chapter covers
- An introduction to the scikit-learn library
- A description and examples of the Isolation Forest, local outlier factor, one-class Support Vector Machine, and Elliptic Envelope detectors
- A description of three other tools provided by scikit-learn: BallTree, KDTree, and Gaussian mixture models
- How to most effectively use these
- Where it is most appropriate to use each
We now have a good general understanding of outlier detection, some specific algorithms, and how outlier detection projects proceed. We will now look at the standard libraries for outlier detection, which will provide the majority, if not all, of the tools you will need for most outlier detection projects, at least for tabular data. Understanding these libraries well will be a major step toward being able to effectively execute outlier detection projects.