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You're reading from R Data Mining Implement data mining techniques through practical use cases and real-world datasets

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781787124462

Length 442 pages

Edition 1st Edition

Languages

Tools

ggplot

Concepts

Data Mining

Author (1):

Andrea Cirillo

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Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Why to Choose R for Your Data Mining and Where to Start FREE CHAPTER

2. A First Primer on Data Mining Analysing Your Bank Account Data

3. The Data Mining Process - CRISP-DM Methodology

4. Keeping the House Clean – The Data Mining Architecture

5. How to Address a Data Mining Problem – Data Cleaning and Validation

6. Looking into Your Data Eyes – Exploratory Data Analysis

7. Our First Guess – a Linear Regression

8. A Gentle Introduction to Model Performance Evaluation

9. Don't Give up – Power up Your Regression Including Multiple Variables

10. A Different Outlook to Problems with Classification Models

11. The Final Clash – Random Forests and Ensemble Learning

12. Looking for the Culprit – Text Data Mining with R

13. Sharing Your Stories with Your Stakeholders through R Markdown

14. Epilogue

15. Dealing with Dates, Relative Paths and Functions

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...