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Mastering Apache Spark 2.x - Second Edition

You're reading from  Mastering Apache Spark 2.x - Second Edition

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Pages 354 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (21) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. A First Taste and What’s New in Apache Spark V2 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Classification with Naive Bayes


This section will provide a working example of the Apache Spark MLlib Naive Bayes algorithm. It will describe the theory behind the algorithm and will provide a step-by-step example in Scala to show how the algorithm may be used.

Theory on Classification

In order to use the Naive Bayes algorithm to classify a dataset, the data must be linearly divisible; that is, the classes within the data must be linearly divisible by class boundaries. The following figure visually explains this with three datasets and two class boundaries shown via the dotted lines:

Naive Bayes assumes that the features (or dimensions) within a dataset are independent of one another; that is, they have no effect on each other. The following example considers the classification of e-mails as spam. If you have 100 e-mails, then perform the following:

60% of emails are spam
80% of spam emails contain the word buy
20% of spam emails don't contain the word buy
40% of emails are not spam
10% of non...
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