Mastering Machine Learning with Spark 2.x: Harness the potential of machine learning, through spark

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Detecting Dark Matter - The Higgs-Boson Particle

True or false? Positive or negative? Pass or no pass? User clicks on the ad versus not clicking the ad? If you've ever asked/encountered these questions before then you are already familiar with the concept of binary classification.

At it's core, binary classification - also referred to as binomial classification - attempts to categorize a set of elements into two distinct groups using a classification rule, which in our case, can be a machine learning algorithm. This chapter shows how to deal with it in the context of Spark and big data. We are going to explain and demonstrate:

Spark MLlib models for binary classification including decision trees, random forest, and the gradient boosted machine
Binary classification support in H2O
Searching for the best model in a hyperspace of parameters
Evaluation metrics for binomial...

Key benefits

• Process and analyze big data in a distributed and scalable way

• Write sophisticated Spark pipelines that incorporate elaborate extraction

• Build and use regression models to predict flight delays

Description

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.

Who is this book for?

Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.

What you will learn

• Use Spark streams to cluster tweets online

• Run the PageRank algorithm to compute user influence

• Perform complex manipulation of DataFrames using Spark

• Define Spark pipelines to compose individual data transformations

• Utilize generated models for off-line/on-line prediction

• Transfer the learning from an ensemble to a simpler Neural Network

• Understand basic graph properties and important graph operations

• Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language

• Use K-means algorithm to cluster movie reviews dataset

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