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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 (16) Chapters

Preface

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

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

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

Dimensionality reduction

We are nearly done with the theoretical lesson, let me just tell you about dimensional reduction, since we are going to employ it in a minute to improve our regression model.

Dimensional reduction is a general category including a variety of techniques employed to effectively reduce the number of variables employed to estimate a regression model. Among these techniques, you should be aware of two of them, since they are of quite easy application but rather powerful:

Stepwise regression
Principal component regression

Stepwise regression

When facing a wide enough range of explanatory variables, like we are now with our customer data, a reasonable question that should probably pop up is: Which is the subset of variables that maximizes the model's performance? Stepwise regression tries to answer that question.

It consists of a set of incremental procedures, from which the step part of the name comes, where a different combination of variables are tried out to find out the...