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Machine Learning with the Elastic Stack - Second Edition

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

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Pages 450 pages
Edition 2nd Edition
Languages
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
Camilla Montonen Camilla Montonen
Profile icon Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
View More author details

Table of Contents (19) Chapters

Preface 1. Section 1 – Getting Started with Machine Learning with Elastic Stack
2. Chapter 1: Machine Learning for IT 3. Chapter 2: Enabling and Operationalization 4. Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
5. Chapter 3: Anomaly Detection 6. Chapter 4: Forecasting 7. Chapter 5: Interpreting Results 8. Chapter 6: Alerting on ML Analysis 9. Chapter 7: AIOps and Root Cause Analysis 10. Chapter 8: Anomaly Detection in Other Elastic Stack Apps 11. Section 3 – Data Frame Analysis
12. Chapter 9: Introducing Data Frame Analytics 13. Chapter 10: Outlier Detection 14. Chapter 11: Classification Analysis 15. Chapter 12: Regression 16. Chapter 13: Inference 17. Other Books You May Enjoy Appendix: Anomaly Detection Tips

Dissecting the detector

At the heart of the anomaly detection job are the analysis configuration and the detector. The detector has several key components to it:

  • The function
  • The field
  • The partition field
  • The by field
  • The over field

We will go through each in turn to fully understand them all. Note that in the next few sections, however, we will often refer to the actual names of settings within the job configuration as if we were using the advanced job editor or the API. Although it is good to fully understand the nomenclature, as you progress through this chapter you will also notice that many of the details of the job configuration are abstracted away from the user or are given more "UI-friendly" labels than the real setting names.

The function

The detector function describes how the data will be aggregated or measured within the analysis interval (bucket span). There are many functions, but they can be classified into the following...

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