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

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 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

Hyperparameter tuning for outlier detection

For the more advanced user, the Data Frame Analytics wizard offers an opportunity to configure and tune hyperparameters – various knobs and dials that fine-tune how the outlier detection algorithm works. The available hyperparameters are displayed in Figure 10.17. For example, we can direct the outlier detection job to use only a certain type of outlier detection method instead of the ensemble, to use a certain value for the number of nearest neighbors that are used in the computation in the ensemble, and to assume that a certain portion of the data is outlying.

Please note that while it is good to play around with these settings to experiment and get a feel for how they affect the final results, if you want to customize any of these for a production usecase, you should carefully study the characteristics of your data and have an awareness of how these characteristics will interact with your chosen hyperparameter settings. More...