Machine learning for email
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 Whi...
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| Format: | eBook |
| Language: | English |
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Sebastopol, CA
O'Reilly
2012
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| Edition: | 1st ed |
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Machine generated contents note: 1. Using R
- R for Machine Learning
- Downloading and Installing R
- IDEs and Text Editors
- Loading and Installing R Packages
- R Basics for Machine Learning
- 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
- Weighting from Email Thread Activity
- Training and Testing the Ranker
- Includes bibliographical references (pages 129-130)