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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav
Toc

Table of Contents (19) Chapters close

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 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

Creating machine learning pipelines using ML


Spark ML is a new library in Spark to build machine learning pipelines. This library is being developed along with MLlib. It helps to combine multiple machine learning algorithms into a single pipeline, and uses DataFrame as dataset.

Getting ready

Let's first understand some of the basic concepts in Spark ML. It uses transformers to transform one DataFrame into another DataFrame. One example of simple transformations can be to append a column. You can think of it as being equivalent to "alter table" in relational world.

Estimator, on the other hand, represents a machine learning algorithm, which learns from the data. Input to an estimator is a DataFrame and output is a transformer. Every Estimator has a fit() method, which does the job of training the algorithm.

A machine learning pipeline is defined as a sequence of stages; each stage can be either an estimator or a transformer.

The example we are going to use in this recipe is whether someone is...

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