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Machine Learning with Spark. - Second Edition

You're reading from  Machine Learning with Spark. - Second Edition

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781785889936
Pages 532 pages
Edition 2nd Edition
Languages
Authors (2):
Rajdeep Dua Rajdeep Dua
Profile icon Rajdeep Dua
Manpreet Singh Ghotra Manpreet Singh Ghotra
Profile icon Manpreet Singh Ghotra
View More author details

Table of Contents (13) Chapters

Preface 1. Getting Up and Running with Spark 2. Math for Machine Learning 3. Designing a Machine Learning System 4. Obtaining, Processing, and Preparing Data with Spark 5. Building a Recommendation Engine with Spark 6. Building a Classification Model with Spark 7. Building a Regression Model with Spark 8. Building a Clustering Model with Spark 9. Dimensionality Reduction with Spark 10. Advanced Text Processing with Spark 11. Real-Time Machine Learning with Spark Streaming 12. Pipeline APIs for Spark ML

Building a Classification Model with Spark

In this chapter, you will learn the basics of classification models, and how they can be used in a variety of contexts. Classification generically refers to classifying things into distinct categories or classes. In the case of a classification model, we typically wish to assign classes based on a set of features. The features might represent variables related to an item or object, an event or context, or some combination of these.

The simplest form of classification is when we have two classes; this is referred to as binary classification. One of the classes is usually labeled as the positive class (assigned a label of 1), while the other is labeled as the negative class (assigned a label of -1, or, sometimes, 0). A simple example with two classes is shown in the following figure. The input features, in this case, have two dimensions, and the feature values are represented...

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