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

Applying the Poisson model for generalized linear regression


Generalized linear models allow response variables that have error distribution models other than a normal distribution (Gaussian). In this recipe, we will demonstrate how to apply Poisson as a family object within glm with regard to count data.

Getting ready

The prerequisite for this task is to prepare the count data, with all the input data values as integers.

How to do it...

Perform the following steps to fit the generalized linear regression model with the Poisson model:

  1. Load the warpbreaks data, and use head to view the first few lines:
       > data(warpbreaks)
       > head(warpbreaks)
       Output:
           breaks wool tension
        1   26     A      L
        2   30     A      L
        3   54     A      L
        4   25     A      L
        5   70     A      L
        6   52     A      L  
  1. We apply Poisson as a family object for the independent variable, tension, and the dependent variable, breaks:
        > rs1...