Mastering Machine Learning with R

图书信息

作者	Cory Lesmeister
出版社	Packt Publishing
ISBN	9781783984534
出版时间	2015-10-28
字数	183.8万
分类	进口书,外文原版书,电脑,网络

读书简介

Master machine learning techniques with R to deliver insights for complex projectsAbout This BookGet to grips with the application of Machine Learning methods using an extensive set of R packagesUnderstand the benefits and potential pitfalls of using machine learning methodsImplement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful.What You Will LearnGain deep insights to learn the applications of machine learning tools to the industryManipulate data in R efficiently to prepare it for analysisMaster the skill of recognizing techniques for effective visualization of dataUnderstand why and how to create test and training data sets for analysisFamiliarize yourself with fundamental learning methods such as linear and logistic regressionComprehend advanced learning methods such as support vector machinesRealize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

目录

Mastering Machine Learning with R

Table of Contents

Mastering Machine Learning with R

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

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