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

Using regression analysis to predict house prices

In the previous chapter, we examined the first of the two supervised learning methods in the Elastic Stack – classification. The goal of classification analysis is to use a labeled dataset to train a model that can predict a class label for a previously unseen datapoint. For example, we could train a model on historical measurements of cell samples coupled with information about whether or not the cell was malignant and use this to predict the malignancy of previously unseen cells. In classification, the class or dependent variable that we are interested in predicting is always a discrete quantity. In regression, on the other hand, we are interested in predicting a continuous variable.

Before we examine the theoretical underpinnings of regression a bit closer, let's dive right in and do a practical walk-through of how to train a regression model in Elasticsearch. The dataset we will be using is available on Kaggle (https...