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Machine Learning for Email
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Machine Learning for Email
by John Myles White, Drew Conway
Machine Learning for Email
Preface
Machine Learning for Hackers: Email
How This Book is Organized
Conventions Used in This Book
Using Code Examples
Safari® Books Online
How to Contact Us
1. Using R
R for Machine Learning
Downloading and Installing R
Windows
Mac OS X
Linux
IDEs and Text Editors
Loading and Installing R Packages
R Basics for Machine Learning
Loading libraries and the data
Converting date strings, and dealing with malformed data
Organizing location data
Dealing with data outside our scope
Aggregating and organizing the data
Analyzing the data
Further Reading on R
2. Data Exploration
Exploration vs. Confirmation
What is Data?
Inferring the Types of Columns in Your Data
Inferring Meaning
Numeric Summaries
Means, Medians, and Modes
Quantiles
Standard Deviations and Variances
Exploratory Data Visualization
Modes
Skewness
Thin Tails vs. Heavy Tails
Visualizing the Relationships between Columns
3. Classification: Spam Filtering
This or That: Binary Classification
Moving Gently into Conditional Probability
Writing Our First Bayesian Spam Classifier
Defining the Classifier and Testing It with Hard Ham
Testing the Classifier Against All Email Types
Improving the Results
4. Ranking: Priority Inbox
How Do You Sort Something When You Don’t Know the Order?
Ordering Email Messages by Priority
Priority Features Email
Writing a Priority Inbox
Functions for Extracting the Feature Set
Creating a Weighting Scheme for Ranking
A Log-Weighting Scheme
Weighting from Email Thread Activity
Training and Testing the Ranker
Works Cited
About the Authors
Copyright
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Preface
Machine Learning for Email
Drew Conway
John Myles White
Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo
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