Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
14
Epilogue

Dimensionality reduction


We are nearly done with the theoretical lesson, let me just tell you about dimensional reduction, since we are going to employ it in a minute to improve our regression model.

Dimensional reduction is a general category including a variety of techniques employed to effectively reduce the number of variables employed to estimate a regression model.  Among these techniques, you should be aware of two of them, since they are of quite easy application but rather powerful:

  • Stepwise regression
  • Principal component regression

Stepwise regression

When facing a wide enough range of explanatory variables, like we are now with our customer data, a reasonable question that should probably pop up is: Which is the subset of variables that maximizes the model's performance? Stepwise regression tries to answer that question. 

It consists of a set of incremental procedures, from which the step part of the name comes, where a different combination of variables are tried out to find out the...