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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Doing classification with Naïve Bayes


Let's consider building an e-mail spam filter using machine learning. Here we are interested in two classes: spam for unsolicited messages and non-spam for regular emails:

The first challenge is that, when given an e-mail, how do we represent it as feature vector x. An e-mail is just bunch of text or a collection of words (therefore, this problem domain falls into a broader category called text classification). Let's represent an e-mail with a feature vector with the length equal to the size of the dictionary. If a given word in a dictionary appears in an e-mail, the value will be 1; otherwise 0. Let's build a vector representing e-mail with the content online pharmacy sale:

The dictionary of words in this feature vector is called vocabulary and the dimensions of the vector are the same as the size of vocabulary. If the vocabulary size is 10,000, the possible values in this feature vector will be 210,000.

Our goal is to model the probability of x given...

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