Outlier Detection in Python, MEAP

Outlier Detection in Python, MEAP.
    
   Outlier detection is an important area of machine learning, and I believe this book is a valuable resource to understand the field itself and to understand how to effectively conduct outlier detection projects. It goes through the purposes for outlier detection, the common tools, how they work, their limitations, practical considerations to produce meaningful results, methods to combine detectors, and methods to evaluate results. The book also puts an emphasis on interpretability, allowing users to understand why some items may be considered statistically more unusual than other items.




Why do outlier detection?
Performing outlier detection is very often quite useful and can usually be applied anywhere we have a significant volume of data. As such, outlier detection is used broadly, so we cannot look at everywhere it’s used, but we’ll look at a few applications, which should provide some sense of how it works and where it can be used generally. One place where outlier detection is ubiquitous is finance, where it is used extensively by investors, fund managers, and regulators. It’s used particularly to find fraud, but also to help identify stocks or funds that are out of line with expectations or normal market trends.

Outlier detection is often used in video surveillance; it can be useful, for example, in situations where the same types of objects tend to be shown repeatedly and anything not normally seen may be of interest. With security video, this can relate to any unusual objects appearing where they are not normally seen; with traffic video, this may identify unusual trajectories, such as vehicles driving erratically. Outlier detection can be used to detect forgery, as forged documents may be unusual relative to legitimate documents. For years, e-commerce websites have been able to distinguish bots from legitimate users by looking at unusual behavior such as clicking at very high rates, viewing very large numbers of pages, or more subtle deviations from normal usage of the site.

Contents.
Welcome.
1 Introduction to outlier detection.
2 Simple outlier detection.
3 Machine learning-based outlier detection.
4 The outlier detection process.
5 Outlier detection using scikit-learn.
6 The PyOD library.
7 Additional libraries and algorithms for outlier detection.



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