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

Applying outlier detection in practice

In this section, we will take a look at a practical example of outlier detection using a public dataset describing the physicochemical properties of wine. This dataset is available for download from the University of California Irvine (UCI) repository (https://archive.ics.uci.edu/ml/datasets/wine+quality).

The wine dataset is composed of two CSV files: one describing the physicochemical properties of white wine, the other those of red wine. In this walk-through, we will be focusing on the white wine dataset, but you are welcome to use the data for red wine as well since most of the steps described in this chapter should be applicable to both.

First let's import the dataset into our Elasticsearch cluster using the Data Visualizer tool, which you can find under the Machine Learning app in Kibana. We will make an index for the white wine dataset and call it winequality-white:

Figure 10.7 – The...