Book Image

Mastering Machine Learning with Spark 2.x

By : Michal Malohlava, Alex Tellez, Max Pumperla
Book Image

Mastering Machine Learning with Spark 2.x

By: Michal Malohlava, Alex Tellez, Max Pumperla

Overview of this book

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.
Table of Contents (9 chapters)
Ensemble Methods for Multi-Class Classification

GraphX distributed graph processing engine

Along with Spark MLlib for machine learning, which we have already encountered a few times in this book, and others like Spark Streaming, which we will cover in Chapter 8, Lending Club Loan Prediction, Spark GraphX is one of the core components of the Spark ecosphere. GraphX is tailored for processing large graphs in an efficient way by building on top of RDDs.

Using the nomenclature developed in the last section, a graph in GraphX is a finite multigraph with loops, where by graph, we actually mean the property graph extension discussed earlier. Next, we will see how graphs are built internally in GraphX.

For the examples used, we recommend firing up spark-shell locally, which will automatically provide dependencies for GraphX. To test whether this works properly in your setup, try importing the full GraphX core module using Scala&apos...