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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

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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

Logistic regression with L-BFGS optimization in Spark 2.0

In this recipe, we will use the UCI admission dataset again so can demonstrate Spark's RDD-based logistic regression solution, LogisticRegressionWithLBFGS(), for an extremely large number of parameters that are present in certain types of ML problem.

We recommend L-BFGS for very large variable space since the Hessian matrix of second derivatives can be approximated using updates. If you have an ML problem with millions or billions of parameters, we recommend deep learning techniques.

How to do it...

We use the admission dataset from UCLA IDRE. You can download the entire dataset from the following URLs:
- For home page go through the http://www.ats.ucla.edu/stat/link.
- For the data file go through the https://stats.idre.ucla.edu/stat/data/binary.csvlink.

The dataset comprises four columns, with the first column being the dependent variable (that is, the label - whether the student was admitted or not) and the next three columns being the...