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

Doc2vec explained

As we mentioned in the chapter's introduction, there is an extension of word2vec that encodes entire documents as opposed to individual words. In this case, a document is what you make of it, be it a sentence, a paragraph, an article, an essay, and so on. Not surprisingly, this paper came out after the original word2vec paper but was also, not surprisingly, coauthored by Tomas Mikolov and Quoc Le. Even though MLlib has yet to introduce doc2vec into their stable of algorithms, we feel it is necessary for a data science practitioner to know about this extension of word2vec, given its promise of and results with supervised learning and information retrieval tasks.

Like word2vec, doc2vec (sometimes referred to as paragraph vectors) relies on a supervised learning task to learn distributed representations of documents based on contextual words. Doc2vec is also...