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You're reading from Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Broderick Hall

Meenakshi Rajendran

Shuen Mei

Mohammed Guller

Siamak Amirghodsi

+1 more

View More author details

Table of Contents (14) Chapters

Preface

1. Practical Machine Learning with Spark Using Scala FREE CHAPTER

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Support Vector Machine (SVM) with Spark 2.0

In this recipe, we use Spark's RDD-based SVM API SVMWithSGD with SGD to classify the population into two binary classes, and then use count and BinaryClassificationMetrics to look at model performance.

In the interest of time and space, we use the sample LIBSVM format supplied with Spark, but provide links to additional data files offered by National Taiwan University so the reader can experiment on their own. Support Vector Machine (SVM) as a concept is fundamentally very simple, unless you want to get into the details of its implementation in Spark or any other package.

While the mathematics behind SVM is beyond the scope of this book, readers are encouraged to read the following tutorials and the original SVM paper for a deeper understanding.

The original papers are by Vapnik and Chervonenkis (1974, 1979 - in Russian) and there's also Vapnik's 1982 translation of his 1979 book:

https://www.amazon.com/Statistical-Learning-Theory-Vladimir-Vapnik...