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If you’re an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation.
This book also includes a short tutorial on using the popular R language to manipulate and analyze data. You’ll get clear examples for analyzing sample data and writing machine learning programs with R.
* Mine email content with R functions, using a collection of sample files
* Analyze the data and use the results to write a Bayesian spam classifier
* Rank email by importance, using factors such as thread activity
* Use your email ranking analysis to write a priority inbox program
* Test your classifier and priority inbox with a separate email sample set
List of contents
Preface
Chapter 1: Using R
Chapter 2: Data Exploration
Chapter 3: Classification: Spam Filtering
Chapter 4: Ranking: Priority Inbox
Works Cited
About the author
Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities. John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.
Summary
This compact book explores standard tools for text classification, and teaches the reader how to use machine learning to decide whether a e-mail is spam or ham (binary classification), based on raw data from The SpamAssassin Public Corpus.
