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

Chapter 10. A Different Outlook to Problems with Classification Models

Now that you have the instruments to interpret the results of data mining models, it is time to move on to executing the data modeling strategy you defined with Andy. Here, you will look at classification models, first of all understanding why they were developed and in what kinds of problems they can be useful.

You will then look at three of the most common models employed within this field, which are logistic regression, support vector machines, and random forest, carefully evaluating what the assumptions are to be satisfied in order for the model to be useful.

One note of warning before leaving you again with Andy—some of the models we are going to see here as classification models are actually sometimes employed, with slight modifications, as regression models. You should therefore not be too rigid in classifying those models into your memory. The same holds for these models being supervised, since unsupervised versions...