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Mastering Machine Learning with R
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Mastering Machine Learning with R
by Cory Lesmeister
Mastering Machine Learning with R
Mastering Machine Learning with R
Table of Contents
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
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Preface
Machine learning defined
Machine learning caveats
Failure to engineer features
Overfitting and underfitting
Causality
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
eBooks, discount offers, and more
Questions
1. A Process for Success
The process
Business understanding
Identify the business objective
Assess the situation
Determine the analytical goals
Produce a project plan
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
2. Linear Regression – The Blocking and Tackling of Machine Learning
Univariate linear regression
Business understanding
Multivariate linear regression
Business understanding
Data understanding and preparation
Modeling and evaluation
Other linear model considerations
Qualitative feature
Interaction term
Summary
3. Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Business understanding
Data understanding and preparation
Modeling and evaluation
The logistic regression model
Logistic regression with cross-validation
Discriminant analysis overview
Discriminant analysis application
Model selection
Summary
4. Advanced Feature Selection in Linear Models
Regularization in a nutshell
Ridge regression
LASSO
Elastic net
Business case
Business understanding
Data understanding and preparation
Modeling and evaluation
Best subsets
Ridge regression
LASSO
Elastic net
Cross-validation with glmnet
Model selection
Summary
5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Business understanding
Data understanding and preparation
Modeling and evaluation
KNN modeling
SVM modeling
Model selection
Feature selection for SVMs
Summary
6. Classification and Regression Trees
Introduction
An overview of the techniques
Regression trees
Classification trees
Random forest
Gradient boosting
Business case
Modeling and evaluation
Regression tree
Classification tree
Random forest regression
Random forest classification
Gradient boosting regression
Gradient boosting classification
Model selection
Summary
7. Neural Networks
Neural network
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
H2O background
Data preparation and uploading it to H2O
Create train and test datasets
Modeling
Summary
8. Cluster Analysis
Hierarchical clustering
Distance calculations
K-means clustering
Gower and partitioning around medoids
Gower
PAM
Business understanding
Data understanding and preparation
Modeling and evaluation
Hierarchical clustering
K-means clustering
Clustering with mixed data
Summary
9. Principal Components Analysis
An overview of the principal components
Rotation
Business understanding
Data understanding and preparation
Modeling and evaluation
Component extraction
Orthogonal rotation and interpretation
Creating factor scores from the components
Regression analysis
Summary
10. Market Basket Analysis and Recommendation Engines
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
User-based collaborative filtering
Item-based collaborative filtering
Singular value decomposition and principal components analysis
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Summary
11. Time Series and Causality
Univariate time series analysis
Bivariate regression
Granger causality
Business understanding
Data understanding and preparation
Modeling and evaluation
Univariate time series forecasting
Time series regression
Examining the causality
Summary
12. Text Mining
Text mining framework and methods
Topic models
Other quantitative analyses
Business understanding
Data understanding and preparation
Modeling and evaluation
Word frequency and topic models
Additional quantitative analysis
Summary
A. R Fundamentals
Introduction
Getting R up and running
Using R
Data frames and matrices
Summary stats
Installing and loading the R packages
Summary
Index
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