Introduction to Machine Learning with R

Preface
Who Should Read This Book?
Scope of the Book
Conventions Used in This Book
O’Reilly Safari
How to Contact Us
Acknowledgments

1. What Is a Model?
Algorithms Versus Models: What’s the Difference?
A Note on Terminology
Modeling Limitations
Statistics and Computation in Modeling
Data Training
Cross-Validation
Why Use R?
The Good
R and Machine Learning
The Bad
Summary

2. Supervised and Unsupervised Machine Learning
Supervised Models
Regression
Training and Testing of Data
Classification
Logistic Regression
Supervised Clustering Methods
Mixed Methods
Tree-Based Models
Random Forests
Neural Networks
Support Vector Machines
Unsupervised Learning
Unsupervised Clustering Methods
Summary

3. Sampling Statistics and Model Training in R
Bias
Sampling in R
Training and Testing
Roles of Training and Test Sets
Why Make a Test Set?
Training and Test Sets: Regression Modeling
Training and Test Sets: Classification Modeling
Cross-Validation
k-Fold Cross-Validation
Summary

4. Regression in a Nutshell
Linear Regression
Multivariate Regression
Regularization
Polynomial Regression
Goodness of Fit with Data—The Perils of Overfitting
Root-Mean-Square Error
Model Simplicity and Goodness of Fit
Logistic Regression
The Motivation for Classification
The Decision Boundary
The Sigmoid Function
Binary Classification
Multiclass Classification
Logistic Regression with Caret
Summary
Linear Regression
Logistic Regression

5. Neural Networks in a Nutshell
Single-Layer Neural Networks
Building a Simple Neural Network by Using R
Multiple Compute Outputs
Hidden Compute Nodes
Multilayer Neural Networks
Neural Networks for Regression
Neural Networks for Classification
Neural Networks with caret
Regression
Classification
Summary

6. Tree-Based Methods
A Simple Tree Model
Deciding How to Split Trees
Tree Entropy and Information Gain
Pros and Cons of Decision Trees
Tree Overfitting
Pruning Trees
Decision Trees for Regression
Decision Trees for Classification
Conditional Inference Trees
Conditional Inference Tree Regression
Conditional Inference Tree Classification
Random Forests
Random Forest Regression
Random Forest Classification
Summary

7. Other Advanced Methods
Naive Bayes Classification
Bayesian Statistics in a Nutshell
Application of Naive Bayes
Principal Component Analysis
Linear Discriminant Analysis
Support Vector Machines
k-Nearest Neighbors
Regression Using kNN
Classification Using kNN
Summary

8. Machine Learning with the caret Package
The Titanic Dataset
Data Wrangling
caret Unleashed
Imputation
Data Splitting
caret Under the Hood
Model Training
Comparing Multiple caret Models
Summary

A. Encyclopedia of Machine Learning Models in caret

Index