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

Moving from simple to multiple linear regression


How would you expand a simple model into a multiple one? I know you are guessing it, by adding more slopes. This is actually the right answer, even if its implications are not that trivial.

Notation

We formally define a multivariate linear model as:

But what is the actual meaning of this formula? We know that the meaning for the univariate was the relationship between an increase of x and an increase of y, but what is the meaning now that we are dealing with multiple variables? 

Once we adopt the ordinary least squares (OLS) again to estimate those coefficients, it turns out that it means how an increase in the given variable influences the level of y, keeping all other variables constant. We therefore, are not dealing with a dynamic model able to express the level of influence of each variable taking into consideration the level of other variables. For the sake of completeness, I have to tell you that it would also be possible to add interactions...