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)
3
Ensemble Methods for Multi-Class Classification

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...