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

Product typeBook

Published inMay 2021

Reading LevelBeginner

PublisherPackt

ISBN-139781801070034

Edition2nd Edition

Languages

Python

Tools

Elasticsearch

Concepts

Machine Learning

Authors (3):

Rich Collier

Camilla Montonen

Bahaaldine Azarmi

View More author details

Applying outlier detection in practice

In this section, we will take a look at a practical example of outlier detection using a public dataset describing the physicochemical properties of wine. This dataset is available for download from the University of California Irvine (UCI) repository (https://archive.ics.uci.edu/ml/datasets/wine+quality).

The wine dataset is composed of two CSV files: one describing the physicochemical properties of white wine, the other those of red wine. In this walk-through, we will be focusing on the white wine dataset, but you are welcome to use the data for red wine as well since most of the steps described in this chapter should be applicable to both.

First let's import the dataset into our Elasticsearch cluster using the Data Visualizer tool, which you can find under the Machine Learning app in Kibana. We will make an index for the white wine dataset and call it winequality-white:

Figure 10.7 – The...

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Machine Learning with the Elastic Stack - Second Edition

Published in: May 2021Publisher: PacktISBN-13: 9781801070034

Authors (3)

Rich Collier

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.
Read more about Rich Collier

Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

Bahaaldine Azarmi

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.
Read more about Bahaaldine Azarmi

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Applying outlier detection in practice

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