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

Logistic regression with SGD optimization in Spark 2.0


In this recipe, we use admission data the UCI Machine Library Repository to build and then train a model to predict student admissions based on a given set of features (GRE, GPA, and Rank) used during the admission process using the RDD-based LogisticRegressionWithSGD() Apache Spark API set.

This recipe demonstrates both optimization (SGD) and regularization (penalizing the model for complexity or over-fitting). We emphasize that they are two different things and often cause confusion to beginners. In the upcoming chapter, we demonstrate both concepts in more detail since understanding both is fundamental to a successful study of ML.

How to do it...

  1. We use the dataset from the UCLA Institute for Digital ResearchandEducation (IDRE). You can download the entire dataset from the following URLs:

The...