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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

Last Updated in Feb 2025

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Amirghodsi

Mohammed Guller

Shuen Mei

Rajendran

Hall

+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 L-BFGS in Spark 2.0

In this recipe, we will demonstrate the use of Spark 2.0's LinearRegression() API to showcase a unified/parameterized API to tackle the linear in a comprehensive capable of extension without backward-compatibility issues of an RDD-based named API. We show how to use the setSolver() to set the optimization method to first-order memory-efficient L-BFGS, which can deal with numerous amount of parameters (that is, especially in sparse configuration) with ease.

Note

In this recipe, the .setSolver() is set to lbgfs, which makes the L-BFGS (see RDD-based regression for more detail) the selected optimization method. The .setElasticNetParam() is not set, so the default of 0 remains in effect, which makes this a Lasso regression.