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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Concepts
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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 (13) Chapters Close

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

Understanding Spark ML


Spark ML is a nickname for the DataFrame-based MLLib API. Spark ML is the primary library now, and the RDD-based API has been moved to maintenance mode.  

Getting ready

Let's first understand some of the basic concepts in Spark ML. Before that, let's quickly go over how the learning process works. Following are the steps:

  1. A machine learning algorithm is provided a training dataset along with the right hyperparameters. 
  2. The result of training is a model. The following figure illustrates the model building by applying machine learning algorithm on training data with hyperparameters: 
  1. The model is then used to make predictions on test data as shown here:

In Spark ML, an estimator is provided as a DataFrame (via the fit method), and the output after training is a Transformer:

Now, the Transformer takes one DataFrame as input and outputs another transformed (via the transform method) DataFrame. For example, it can take a DataFrame with the test data and enrich this DataFrame with...

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