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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

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

Coding Gradient Descent optimization to solve Linear Regression from scratch

In this recipe, we will explore how to code Descent to solve a Linear Regression problem. In the previous recipe, we demonstrated how to code GD to find the minimum of a quadratic function.

This recipe demonstrates a more realistic optimization problem in which we optimize (minimize) the least square cost function to solve the linear regression problem in Scala on Apache Spark 2.0+. We will use real data and run our algorithm and compare the result to a tier-1 commercially available statistic software to demonstrate accuracy and speed.

How to do it...

We start by downloading the file from Princeton University which contains the following data:

Source: Princeton University

Download source: http://data.princeton.edu/wws509/datasets/#salary.

To keep things simple, we then select the yr and sl to study how the number of years in rank influences the salary. To cut down on data wrangling code, we save those two columns in...