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Mastering Machine Learning with R - Second Edition

By : Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky

2.8 (4)

Mastering Machine Learning with R

2.8 (4)

By: Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

A Process for Success

A Process for Success

The process

Business understanding

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Algorithm flowchart

Summary

Linear Regression - The Blocking and Tackling of Machine Learning

Linear Regression - The Blocking and Tackling of Machine Learning

Univariate linear regression

Multivariate linear regression

Other linear model considerations

Summary

Logistic Regression and Discriminant Analysis

Logistic Regression and Discriminant Analysis

Classification methods and linear regression

Logistic regression

Discriminant analysis overview

Multivariate Adaptive Regression Splines (MARS)

Model selection

Summary

Advanced Feature Selection in Linear Models

Advanced Feature Selection in Linear Models

Regularization in a nutshell

Business case

Modeling and evaluation

Model selection

Regularization and classification

Summary

More Classification Techniques - K-Nearest Neighbors and Support Vector Machines

More Classification Techniques - K-Nearest Neighbors and Support Vector Machines

K-nearest neighbors

Support vector machines

Business case

Feature selection for SVMs

Summary

Classification and Regression Trees

Classification and Regression Trees

An overview of the techniques

Business case

Summary

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Introduction to neural networks

Deep learning, a not-so-deep overview

Business understanding

Data understanding and preparation

Modeling and evaluation

An example of deep learning

Summary

Cluster Analysis

Cluster Analysis

Hierarchical clustering

K-means clustering

Gower and partitioning around medoids

Random forest

Business understanding

Data understanding and preparation

Modeling and evaluation

Summary

Principal Components Analysis

Principal Components Analysis

An overview of the principal components

Business understanding

Modeling and evaluation

Summary

Market Basket Analysis, Recommendation Engines, and Sequential Analysis

Market Basket Analysis, Recommendation Engines, and Sequential Analysis

An overview of a market basket analysis

Business understanding

Data understanding and preparation

Modeling and evaluation

An overview of a recommendation engine

Business understanding and recommendations

Data understanding, preparation, and recommendations

Modeling, evaluation, and recommendations

Sequential data analysis

Summary

Creating Ensembles and Multiclass Classification

Creating Ensembles and Multiclass Classification

Ensembles

Business and data understanding

Modeling evaluation and selection

Multiclass classification

Business and data understanding

Model evaluation and selection

MLR's ensemble

Summary

Time Series and Causality

Time Series and Causality

Univariate time series analysis

Business understanding

Modeling and evaluation

Summary

Text Mining

Text Mining

Text mining framework and methods

Topic models

Business understanding

Modeling and evaluation

Summary

R on the Cloud

R on the Cloud

Creating an Amazon Web Services account

Summary

R Fundamentals

R Fundamentals

Getting R up-and-running

Using R

Data frames and matrices

Creating summary statistics

Installing and loading R packages

Data manipulation with dplyr

Summary

Sources

Sources

Logistic Regression and Discriminant Analysis

"The true logic of this world is the calculus of probabilities."
- James Clerk Maxwell, Scottish physicist

In the previous chapter, we took a look at using Ordinary Least Squares (OLS) to predict a quantitative outcome, or in other words, linear regression. It is now time to shift gears somewhat and examine how we can develop algorithms to predict qualitative outcomes. Such outcome variables could be binary (male versus female, purchase versus does not purchase, tumor is benign versus malignant) or multinomial categories (education level or eye color). Regardless of whether the outcome of interest is binary or multinomial, the task of the...

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Programming languages

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