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 under the hood: gradient boosted decision trees

The ultimate goal for a classification task is to solve a problem that requires us to take previously unseen data points and try to infer which of the several possible classes they belong to. We achieve this by taking a labeled training dataset that contains a representative number of data points, extracting relevant features that allow us to learn a decision boundary, and then encode the knowledge about this decision boundary into a classification model. This model then makes decisions about which class a given data point belongs to. How does the model learn to do this? This is the question that we will try to answer in this section.

In accordance with our habits throughout the book, let's start by exploring conceptually what tools humans use to navigate a set of complicated decisions. A familiar tool that many of us have used before to help make decisions when several, possibly complex factors are involved, is...