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

Classification: from data to a trained model

The process of training a classification model from a source dataset is a multi-step affair that involves many steps. In this section, we will take a bird's eye view (depicted in Figure 11.1) of this whole process, which begins with a labeled training dataset (Figure 11.1 part A.).

Figure 11.1 – An overview of the supervised learning process that takes a labeled dataset and outputs a trained model

This training dataset is usually split into a training part, which will be fed into the training algorithm (Figure 11.1 part B.). The output of the training algorithm is a trained model (Figure 11.1 part C.). The trained model is then used to classify the testing dataset (Figure 11.1, part D.), originally set aside from the whole dataset. The performance of the model on the testing dataset is captured in a set of evaluation metrics that can be used to determine whether a model generalizes well enough to previously...