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Machine Learning with the Elastic Stack - Second Edition

You're reading from  Machine Learning with the Elastic Stack - Second Edition

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Pages 450 pages
Edition 2nd Edition
Languages
Concepts
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
LinkedIn
Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
See other products by Rich Collier
Camilla Montonen Camilla Montonen
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
LinkedIn
Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
See other products by Bahaaldine Azarmi
View More author details

Table of Contents (19) Chapters

Preface Section 1 – Getting Started with Machine Learning with Elastic Stack
Chapter 1: Machine Learning for IT Chapter 2: Enabling and Operationalization Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
Chapter 3: Anomaly Detection Chapter 4: Forecasting Chapter 5: Interpreting Results Chapter 6: Alerting on ML Analysis Chapter 7: AIOps and Root Cause Analysis Chapter 8: Anomaly Detection in Other Elastic Stack Apps Section 3 – Data Frame Analysis
Chapter 9: Introducing Data Frame Analytics Chapter 10: Outlier Detection Chapter 11: Classification Analysis Chapter 12: Regression Chapter 13: Inference 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...

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Authors (3)

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Profile icon Rich Collier
LinkedIn
Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.
See other products by Rich Collier
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Profile icon Bahaaldine Azarmi
LinkedIn
Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
See other products by Bahaaldine Azarmi
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