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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

Understanding influencers in split versus non-split jobs

You might question whether or not it is necessary to split the analysis by a field, or merely hope that the use of influencers will give the desired effect of identifying the offending entity.

Let's remind ourselves of the difference between the purpose of influencers and the purpose of splitting a job. An entity is identified by an anomaly detection job as an influencer if it has contributed significantly to the existence of the anomaly. This notion of deciding influential entities is completely independent of whether or not the job is split. An entity can be deemed influential on an anomaly only if an anomaly happens in the first place. If there is no anomaly detected, there is no need to figure out whether there is an influencer. However, the job may or may not find that something is anomalous, depending on whether or not the job is split into multiple time series. When splitting the job, you are modeling (creating...

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