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

Using decision trees for regression

As we have discussed in the preceding chapters, regression is a supervised learning technique. As discussed in Chapter 11, Classification Analysis, the goal of supervised learning is to take a labeled dataset (for example, a dataset that has features of houses and their sales price – the dependent variable) and distill the knowledge in this data into an artifact known as a trained model. This trained model can then be used to predict the sales prices of houses that the model has not previously seen. When the dependent variable that we are trying to predict is a continuous variable, as opposed to a discrete variable, which is the domain of classification, we are dealing with regression.

Regression – the task of distilling the information presented in real-world observations or data – is a field of machine learning that encompasses techniques far broader than the decision tree technique that is used in Elasticsearch's...