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

Summary


This is the author speaking here. Did you solve the mystery of the revenues drop? You did not, I guess. Nevertheless, you made some relevant steps on your journey to learning how to use R for data mining activities. 

In this chapter, you learned some conceptual and some practical stuff, and you now possess medium-level skills to define and measure the performance of data mining models.

Andy first explained to you what we do intend for model performance and how this concept is related to the one of model interpretability and the purposes for which the model was estimated.

You then learned what the main model metrics are for both regression and classification problems.

Firstly, you were introduced to the relevant concepts of error, mean squared errors, and R-squared.

About this latter statistics, you also carefully analyzed its meaning and the common misconceptions regarding it. I strongly advise you to carefully hold these misconceptions in your mind, since in your everyday professional...