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

Building a classification system with Random Forest Trees in Spark 2.0


In this recipe, we will explore Random Forest in Spark. We will use the Random Forest technique to solve a discrete classification problem. We random forest implementation very fast due to Spark's exploitation of parallelism (growing many trees at once). We also do not need to worry too much about the hyper-parameters and technically we can get away with just setting the number of trees.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.
  1. Set up the package location where the program will reside:
package spark.ml.cookbook.chapter10
  1. Import the necessary packages for the Spark context to get access to the cluster and Log4j.Logger to reduce the amount of output produced by Spark:
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache...