Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Exploring ML pipelines and DataFrames using logistic regression in Spark 2.0


We have gone out of our way to present the in detail and as as possible so you get started without the additional syntactic sugar that Scala uses.

Getting ready

In this recipe, we combine the ML pipelines and logistic regression to demonstrate how you can combine various steps in a single pipeline that operates on DataFrames as they get transformed and travel through the pipe. We skip some of the steps, such as splitting the data and model evaluation, and reserve them for later chapters to make the program shorter, but provide a full treatment of pipeline, DataFrame, estimators, and transformers in a single recipe.

This recipe explores the details of the pipeline and DataFrames as they travel through the pipeline and get operated on.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
  1. Set up the package location where the program will reside...