Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
5 (1)
Book Image

Machine Learning with the Elastic Stack - Second Edition

5 (1)
By: Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview of this book

Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Table of Contents (19 chapters)
1
Section 1 – Getting Started with Machine Learning with Elastic Stack
4
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
11
Section 3 – Data Frame Analysis

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...