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

Classifying data with the k-nearest neighbor classifier


K-nearest neighbor (knn) is a nonparametric lazy learning method. From a nonparametric view, it does not make any assumptions about data distribution. In terms of lazy learning, it does not require an explicit learning phase for generalization. The following recipe will introduce how to apply the k-nearest neighbor algorithm on the churn dataset.

Getting ready

You need to have the previous recipe completed by generating the training and testing datasets.

How to do it...

Perform the following steps to classify the churn data with the k-nearest neighbor algorithm:

  1. First, one has to install the class package and have it loaded in an R session:
> install.packages("class")> library(class)
  1. Replace yes and no of the voice_mail_plan and international_plan attributes in both the training dataset and testing dataset to 1 and 0:
        > levels(trainset$international_plan) = list("0"="no", "1"="yes")
        > levels(trainset$voice_mail_plan...