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You're reading from  Machine Learning with the Elastic Stack - Second Edition

Product typeBook
Published inMay 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781801070034
Edition2nd Edition
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Authors (3):
Rich Collier
Rich Collier
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Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
Read more about Rich Collier

Camilla Montonen
Camilla Montonen
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Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

Bahaaldine Azarmi
Bahaaldine Azarmi
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Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

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Summary

To conclude the chapter, let's remind ourselves of the main features of the second unsupervised learning feature in the Elastic Stack: outlier detection. Outlier detection can be used to detect unusual data points in single or multidimensional datasets.

The algorithm is based on an ensemble of four separate measures: two distance-based measures based on kth-nearest neighbors and two density-based measures. The combination of these measures captures how far a given data point is from its neighbors and from the general mass of data in the dataset. This unusualness is captured in a numerical outlier score that ranges from 0 to 1. The closer a given data point scores to 1, the more unusual it is in the dataset.

In addition to the outlier score, for each feature or field of a point, we compute a quantity known as the feature influence. The higher the feature influence for a given field, the more that field is responsible for a given point being unusual. These feature...

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Machine Learning with the Elastic Stack - Second Edition
Published in: May 2021Publisher: PacktISBN-13: 9781801070034

Authors (3)

author image
Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
Read more about Rich Collier

author image
Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

author image
Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi