Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook, Second Edition - Second Edition

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.
Table of Contents (21 chapters)
Title Page
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Performing dimension reduction with PCA


Principal Component Analysis (PCA) is the most widely used linear method in dealing with dimension reduction problems. It is useful when data contains many features, and there is redundancy (correlation) within these features. To remove redundant features, PCA maps high dimension data into lower dimensions by reducing features into a smaller number of principal components that account for most of the variance of the original features. In this recipe, we will look at how to perform dimension reduction with the PCA method.

Getting ready

In this recipe, we will use the swiss dataset as our target to perform PCA. The swiss dataset includes standardized fertility measures and socio-economic indicators from around the year 1888 for each of the 47 French-speaking provinces of Switzerland.

How to do it...

Perform the following steps to perform principal component analysis on the swiss dataset:

  1. First, load the swiss dataset:
> data(swiss)
  1. Exclude the first column...