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

Modeling goal

In this example, we would like to build a model based on information about physical activities to classify unseen data and annotate it with the corresponding physical activity.

Challenges

For the sensor data, there are numerous way to explore and build models. In this chapter, we mainly focus on classification; however, there are several aspects which would need deeper exploration, especially the following:

  • Training data represents a time-ordered flow of events, but we are not going to reflect the time information but look at the data as one complete piece of information
  • The same for test data -a single activity event is a part of an event stream captured during performing an activity and it can be easier to...