Mastering Machine Learning with R - Second Edition电子书

Title Page

Copyright

Credits

About the Author

About the Reviewers

Packt Upsell

Customer Feedback

Preface

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

Questions

A Process for Success

The process

Business understanding

Identifying the business objective

Assessing the situation

Determining the analytical goals

Producing a project plan

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Algorithm flowchart

Summary

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 features

Interaction terms

Summary

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

Multivariate Adaptive Regression Splines (MARS)

Model selection

Summary

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

Regularization and classification

Logistic regression example

Summary

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

Classification and Regression Trees

An overview of the techniques

Understanding the regression trees

Classification trees

Random forest

Gradient boosting

Business case

Modeling and evaluation

Regression tree

Classification tree

Random forest regression

Random forest classification

Extreme gradient boosting - classification

Model selection

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

Business understanding

Data understanding and preparation

Modeling and evaluation

An example of deep learning

H2O background

Data upload to H2O

Create train and test datasets

Modeling

Summary

Cluster Analysis

Hierarchical clustering

Distance calculations

K-means clustering

Gower and partitioning around medoids

Gower

PAM

Random forest

Business understanding

Data understanding and preparation

Modeling and evaluation

Hierarchical clustering

K-means clustering

Gower and PAM

Random Forest and PAM

Summary

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

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

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

Sequential data analysis

Sequential analysis applied

Summary

Creating Ensembles and Multiclass Classification

Ensembles

Business and data understanding

Modeling evaluation and selection

Multiclass classification

Business and data understanding

Model evaluation and selection

Random forest

Ridge regression

MLR's ensemble

Summary

Time Series and Causality

Univariate time series analysis

Understanding Granger causality

Business understanding

Data understanding and preparation

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 analyses

Business understanding

Data understanding and preparation

Modeling and evaluation

Word frequency and topic models

Additional quantitative analysis

Summary

R on the Cloud

Creating an Amazon Web Services account

Launch a virtual machine

Start RStudio

Summary

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