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You're reading from Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Broderick Hall

Meenakshi Rajendran

Shuen Mei

Mohammed Guller

Siamak Amirghodsi

+1 more

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Table of Contents (14) Chapters

Preface

1. Practical Machine Learning with Spark Using Scala FREE CHAPTER

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Latent Dirichlet Allocation (LDA) to classify documents and text into topics

In this recipe, we will explore the Latent Dirichlet Allocation (LDA) algorithm in Spark 2.0. The LDA we use in this recipe is completely different from linear analysis. Both Latent Dirichlet Allocation and linear discrimination analysis are referred to as LDA, but they are extremely different techniques. In this recipe, when we use the LDA, we refer to Latent Dirichlet Allocation. The chapter on text analytics is also relevant to understanding the LDA.

LDA is often used in natural language processing which tries to classify a large body of document (for example, emails from the Enron fraud case) into a discrete number of topics or themes so it can be understood. LDA is also a good candidate for selecting articles based on one's interest (for example, as you turn a page and spend time on a specific topic) in a given magazine article or page.