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

Deploying a pattern mining application

The example developed in the last section was an interesting playground to apply the algorithms we have carefully laid out throughout the chapter, but we have to recognize the fact that we were just handed the data. At the time of writing this book, it was often part of the culture in building data products to draw a line in the sand between data science and data engineering at pretty much exactly this point, that is, between real-time data collection and aggregation, and (often offline) analysis of data, followed up by feeding back reports of the insights gained into the production system. While this approach has its value, there are certain drawbacks to it as well. By not taking the full picture into account, we might, for instance, not exactly know the details of how the data has been collected. Missing information like this can lead to...