You're reading from Machine Learning with the Elastic Stack Gain valuable insights from your data with Elastic Stack's machine learning features

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781801070034

Length 450 pages

Edition 2nd Edition

Languages

Python

Tools

Elasticsearch

Concepts

Machine Learning

Authors (3):

Camilla Montonen

Rich Collier

Bahaaldine Azarmi

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Table of Contents (19) Chapters

Preface

1. Section 1 – Getting Started with Machine Learning with Elastic Stack

2. Chapter 1: Machine Learning for IT FREE CHAPTER

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

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...