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

Measuring the prediction performance of a conditional inference tree


After building a conditional inference tree as a classification model, we can use the treeresponse and predict functions to predict categories of the testing dataset, testset, and further validate the prediction power with a classification table and a confusion matrix.

Getting ready

You need to have the previous recipe completed by generating the conditional inference tree model, ctree.model. In addition to this, you need to have both trainset and testset loaded in an R session.

How to do it...

Perform the following steps to measure the prediction performance of a conditional inference tree:

  1. You can use the predict function to predict the category of the testing dataset testset:
        > ctree.predict = predict(ctree.model ,testset)
        > table(ctree.predict, testset$churn)
        Output
        ctree.predict yes no
                  yes 99 15
                  no 42 862  
  1. Furthermore, you can use confusionMatrix from...