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

Visualizing a recursive partitioning tree


From the last recipe, we learned how to print the classification tree in a text format. To make the tree more readable, we can use the plot function to obtain a graphical display of a built classification tree.

Getting ready

One needs to have the previous recipe completed by generating a classification model, and to assign the model into the churn.rp variable.

How to do it...

Perform the following steps to visualize the classification tree:

  1. Use the plot function and the text function to plot the classification tree:
        > plot(churn.rp, margin= 0.1)
        > text(churn.rp, all=TRUE, use.n = TRUE)

The graphical display of a classification tree

  1. You can also specify the uniform, branch, and margin parameter to adjust the layout:
        > plot(churn.rp, uniform=TRUE, branch=0.6, margin=0.1)
        > text(churn.rp, all=TRUE, use.n = TRUE)

Adjust the layout of the classification tree

How it works...

Here, we demonstrate how to use the plot function...