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 logistic regression


Logistic regression is a form of probabilistic statistical classification model, which can be used to predict class labels based on one or more features. The classification is done by using the logit function to estimate the outcome probability. One can use logistic regression by specifying the family as a binomial while using the glm function. In this recipe, we will introduce how to classify data using logistic regression.

Getting ready

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

How to do it...

Perform the following steps to classify the churn data with logistic regression:

  1. With the specification of family as a binomial, we apply the glm function on the dataset trainset, by using churn as a class label and the rest of the variables as input features:
        > fit = glm(churn ~ ., data = trainset, family=binomial)
  1. Use the summary function to obtain summary information of the built logistic regression model...