- Machine Learning with R
- Table of Contents
- Machine Learning with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Introducing Machine Learning
- 2. Managing and Understanding Data
- R data structures
- Vectors
- Factors
- Managing data with R
- Exploring and understanding data
- Exploring the structure of data
- Exploring numeric variables
- Measuring the central tendency – mean and median
- Measuring spread – quartiles and the five-number summary
- Visualizing numeric variables – boxplots
- Visualizing numeric variables – histograms
- Understanding numeric data – uniform and normal distributions
- Measuring spread – variance and standard deviation
- Exploring categorical variables
- Exploring relationships between variables
- Summary
- 3. Lazy Learning – Classification Using Nearest Neighbors
- 4. Probabilistic Learning – Classification Using Naive Bayes
- Understanding naive Bayes
- Example – filtering mobile phone spam with the naive Bayes algorithm
- Summary
- 5. Divide and Conquer – Classification Using Decision Trees and Rules
- Understanding decision trees
- Example – identifying risky bank loans using C5.0 decision trees
- Understanding classification rules
- Example – identifying poisonous mushrooms with rule learners
- Summary
- 6. Forecasting Numeric Data – Regression Methods
- Understanding regression
- Example – predicting medical expenses using linear regression
- Understanding regression trees and model trees
- Example – estimating the quality of wines with regression trees and model trees
- Summary
- 7. Black Box Methods – Neural Networks and Support Vector Machines
- 8. Finding Patterns – Market Basket Analysis Using Association Rules
- 9. Finding Groups of Data – Clustering with k-means
- Understanding clustering
- Clustering as a machine learning task
- The k-means algorithm for clustering
- Finding teen market segments using k-means clustering
- Step 1 – collecting data
- Step 2 – exploring and preparing the data
- Step 3 – training a model on the data
- Step 4 – evaluating model performance
- Step 5 – improving model performance
- Summary
- Understanding clustering
- 10. Evaluating Model Performance
- 11. Improving Model Performance
- 12. Specialized Machine Learning Topics
- Index
Brett Lantz has spent the past 10 years using innovative data methods to understand human behavior. A sociologist by training, he was first enchanted by machine learning while studying a large database of teenagers' social networking website profiles. Since then, he has worked on interdisciplinary studies of cellular telephone calls, medical billing data, and philanthropic activity, among others. When he's not spending time with family, following college sports, or being entertained by his dachshunds, he maintains dataspelunking.com, a website dedicated to sharing knowledge about the search for insight in data.
This book could not have been written without the support of my family and friends. In particular, my wife Jessica deserves many thanks for her patience and encouragement throughout the past year. My son Will (who was born while Chapter 10 was underway), also deserves special mention for his role in the writing process; without his gracious ability to sleep through the night, I could not have strung together a coherent sentence the next morning. I dedicate this book to him in the hope that one day he is inspired to follow his curiosity wherever it may lead.
I am also indebted to many others who supported this book indirectly. My interactions with educators, peers, and collaborators at the University of Michigan, the University of Notre Dame, and the University of Central Florida seeded many of the ideas I attempted to express in the text. Additionally, without the work of researchers who shared their expertise in publications, lectures, and source code, this book might not exist at all. Finally, I appreciate the efforts of the R team and all those who have contributed to R packages, whose work ultimately brought machine learning to the masses.
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