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Machine Learning with the Elastic Stack

You're reading from   Machine Learning with the Elastic Stack Expert techniques to integrate machine learning with distributed search and analytics

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788477543
Length 304 pages
Edition 1st Edition
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Authors (2):
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Bahaaldine Azarmi Bahaaldine Azarmi
Author Profile Icon Bahaaldine Azarmi
Bahaaldine Azarmi
Rich Collier Rich Collier
Author Profile Icon Rich Collier
Rich Collier
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Toc

Table of Contents (12) Chapters Close

Preface Machine Learning for IT FREE CHAPTER Installing the Elastic Stack with Machine Learning Event Change Detection IT Operational Analytics and Root Cause Analysis Security Analytics with Elastic Machine Learning Alerting on ML Analysis Using Elastic ML Data in Kibana Dashboards Using Elastic ML with Kibana Canvas Forecasting ML Tips and Tricks Other Books You May Enjoy

Detecting things that rarely occur

In the context of a stream of temporal information (such as a log file), the notion of something being statistically rare (occurs at a low frequency) is paradoxically both intuitive and hard to understand. If I were asked, for example, to trawl through a log file and find a rare message, I might be tempted to label the first novel message that I saw as a rare one. But what if practically every message was novel? Are they all rare? Or is nothing rare?

In order to define rarity to be useful in the context of a stream of events in time, we need to agree that the declaration of something as being rare must take into account the context in which it exists. If there are lots of other routine things and a small number of unique things, then we can deem the unique things rare. If there are many unique things, then we will deem that nothing is rare.

When...

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