Packt+ | Advance your knowledge in tech

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

Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0

In this recipe, we build on the recipe LinearRegression by selecting LASSO regression explicitly via the setElasticNetParam(0.0) while letting Spark 2.0 pick the optimization on its own using setSolver('auto'). We remind again that the RDD-based regression API is now in maintenance mode and this is the preferred method going forward.

How to do it...

We use a housing data set from the UCI machine library depository..

Download the entire data set from the following URLs:

The dataset is comprised of 14 columns with the first 13 columns being the independent variables (that is, features) that try to explain the median price (that is, last column) of an owner-occupied house in Boston, USA.

We have chosen and cleaned the first eight columns as features. We use the first 200 rows to train and predict the median...