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

Defining model performance


OK then, let's ask a question to start talking about performance: when you estimate a model, how do you say if it is a good model? As you have probably already heard, the American statistician George Box used to say:

All models are wrong, but some are useful.

This is, besides a nice quote, also a great truth: there is no perfect model, all models are some kind of an abstraction from reality, like maps are an abstraction from the real Earth. Nevertheless, if those maps are accurate enough, they are invaluable friends in the hands of travelers. This could seem to you nothing more than a suggestive analogy, but it's actually a useful way to intend models since it captures two of their most relevant aspects:

  • Models need to have a good level of abstraction from the real phenomenon they are trying to model
  • Models have to be accurate enough to be useful

I don't need to say to you that the main topics here are to define what a good level of abstractionis and define what it...