Mastering Machine Learning with R

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Summary

Creating a new package

Creating a Package

Summary

Additional quantitative analysis

LASSO model

Data preparation

Classifying text

Topic models

N-grams

Sentiment analysis

Lincoln's word frequency

Word frequency in all addresses

Word frequency

Data frame creation

Data overview

Other quantitative analysis

Topic models

Text mining framework and methods

Text Mining

Summary

Vector autoregression

Linear regression

Examining the causality

Univariate time series forecasting

Modeling and evaluation

Data exploration

Time series data

Understanding Granger causality

Univariate time series analysis

Time Series and Causality

Summary

Modeling and evaluation

Data preparation

Data understanding

Creating transactional data

An overview of association analysis

Association Analysis

Summary

Test data evaluation

Regression with MARS

Creating scores from the components

Orthogonal rotation and interpretation

Component extraction

PCA modeling

Training and testing datasets

Data loading and review

Data

Rotation

An overview of the principal components

Principal Component Analysis

Summary

Random forest and PAM

Gower and PAM

K-means clustering

Hierarchical clustering

Modeling

Data understanding and preparation

Dataset background

Random forest

PAM

Gower

Gower and PAM

K-means clustering

Distance calculations

Hierarchical clustering

Cluster Analysis

Summary

Creating an ensemble

Random forest model

Modeling and evaluation

Data understanding

Ensembles

Creating Ensembles and Multiclass Methods

Summary

Model training

Creating the model function

Loading the data

Keras and TensorFlow background

An example of deep learning

Modeling and evaluation

Data understanding and preparation

Creating a simple neural network

Deep learning resources and advanced methods

Deep learning – a not-so-deep overview

Introduction to neural networks

Neural Networks and Deep Learning

Summary

Feature selection with random forests

Extreme gradient boosting – classification

Random forest

Classification tree

Datasets and modeling

Gradient boosting

Random forest

Classification trees

Understanding a regression tree

An overview of the techniques

Tree-Based Classification

Summary

Support vector machine

KNN modeling

Modeling and evaluation

Data preparation

Dataset creation

Manipulating data

Support vector machines

K-nearest neighbors

K-Nearest Neighbors and Support Vector Machines

Summary

Elastic net

LASSO

Ridge regression

Modeling and evaluation

Data creation

Elastic net

LASSO

Ridge regression

Regularization overview

Advanced Feature Selection in Linear Models

Summary

Model comparison

Multivariate adaptive regression splines

Cross-validation and logistic regression

Feature selection

Weight of evidence and information value

Training a logistic regression algorithm

Model training and evaluation

Logistic regression

Classification methods and linear regression

Logistic Regression

Summary

Reverse transformation of natural log predictions

Modeling and evaluation – MARS

Modeling and evaluation – stepwise regression

Loading and preparing the data

Multivariate linear regression

Reviewing model assumptions

Building a univariate model

Univariate linear regression

Linear Regression

Summary

Correlation and linearity

Treating the data

Zero and near-zero variance features

Handling missing values

Exploring categorical variables

Descriptive statistics

Handling duplicate observations

Reading the data

Overview

Preparing and Understanding Data

Reviews

Get in touch

Conventions used

Download the color images

Download the example code files

To get the most out of this book

What this book covers

Who this book is for

Preface

Packt is searching for authors like you

About the reviewers

About the author

Contributors

Packt.com

Why subscribe?

About Packt

Mastering Machine Learning with R Third Edition

Copyright and Credits

Title Page

coverpage

coverpage

Title Page

Copyright and Credits

Mastering Machine Learning with R Third Edition

About Packt

Why subscribe?

Packt.com

Contributors

About the author

About the reviewers

Packt is searching for authors like you

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Preparing and Understanding Data

Overview

Reading the data

Handling duplicate observations

Descriptive statistics

Exploring categorical variables

Handling missing values

Zero and near-zero variance features

Treating the data

Correlation and linearity

Summary

Linear Regression

Univariate linear regression

Building a univariate model

Reviewing model assumptions

Multivariate linear regression

Loading and preparing the data

Modeling and evaluation – stepwise regression

Modeling and evaluation – MARS

Reverse transformation of natural log predictions

Summary

Logistic Regression

Classification methods and linear regression

Logistic regression

Model training and evaluation

Training a logistic regression algorithm

Weight of evidence and information value

Feature selection

Cross-validation and logistic regression

Multivariate adaptive regression splines

Model comparison

Summary

Advanced Feature Selection in Linear Models

Regularization overview

Ridge regression

LASSO

Elastic net

Data creation

Modeling and evaluation

Ridge regression

LASSO

Elastic net

Summary

K-Nearest Neighbors and Support Vector Machines

K-nearest neighbors

Support vector machines

Manipulating data

Dataset creation

Data preparation

Modeling and evaluation

KNN modeling

Support vector machine

Summary

Tree-Based Classification

An overview of the techniques

Understanding a regression tree

Classification trees

Random forest

Gradient boosting

Datasets and modeling

Classification tree

Random forest

Extreme gradient boosting – classification

Feature selection with random forests

Summary

Neural Networks and Deep Learning

Introduction to neural networks

Deep learning – a not-so-deep overview

Deep learning resources and advanced methods

Creating a simple neural network

Data understanding and preparation

Modeling and evaluation

An example of deep learning

Keras and TensorFlow background

Loading the data

Creating the model function

Model training

Summary

Creating Ensembles and Multiclass Methods

Ensembles

Data understanding

Modeling and evaluation

Random forest model

Creating an ensemble

Summary

Cluster Analysis

Hierarchical clustering

Distance calculations

K-means clustering

Gower and PAM

Gower

PAM

Random forest

Dataset background

Data understanding and preparation

Modeling

Hierarchical clustering

K-means clustering

Gower and PAM

Random forest and PAM

Summary

Principal Component Analysis

An overview of the principal components

Rotation

Data

Data loading and review

Training and testing datasets

PCA modeling

Component extraction

Orthogonal rotation and interpretation

Creating scores from the components

Regression with MARS

Test data evaluation

Summary

Association Analysis

An overview of association analysis

Creating transactional data

Data understanding

Data preparation

Modeling and evaluation

Summary

Time Series and Causality

Univariate time series analysis

Understanding Granger causality

Time series data

Data exploration

Modeling and evaluation

Univariate time series forecasting

Examining the causality

Linear regression

Vector autoregression

Summary

Text Mining

Text mining framework and methods

Topic models

Other quantitative analysis

Data overview

Data frame creation

Word frequency

Word frequency in all addresses

Lincoln's word frequency

Sentiment analysis

N-grams

Topic models

Classifying text

Data preparation

LASSO model

Additional quantitative analysis

Summary

Creating a Package

Creating a new package

Summary

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