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

Summary

This chapter demonstrated three powerful concepts: the processing of text, Spark pipelines, and super learners.

The text processing is a powerful concept that is waiting to be largely adopted by the industry. Hence, we will go deeper into the topic in the following chapters and look at other approaches of natural language processing.

The same holds for Spark pipelines, which have become an inherent part of Spark and the core of the Spark ML package. They offer an elegant way of reusing the same concepts during training and scoring time. Hence, we would like to use the concept in the upcoming chapters as well.

Finally, with super learners, aka ensembles, you learned the basic concept of how to benefit from ensembling multiple models together with the help of a meta-learner. This offers a simple but powerful way of building strong learners, which are still simple enough...