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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 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

Summary

How was your estimation activity? Andy is showing you both theoretical and practical aspects of what you will come to do, letting you keep them all together.

Within this chapter, you actually learned a lot and you are now able to estimate a simple (one variable) linear regression model and check whether its assumptions are satisfied. This is not to be underestimated for two main reasons:

Simple linear models are quite often an oversimplification of the real relationship between two variables. Nevertheless, they tend to be considered good enough for the level of accuracy requested within many fields, and this is why they are very popular.
You will find that a lot of models estimate without checking for assumptions, and you should remember that estimates coming from an invalid model are invalid estimates, at least for descriptive purposes (more on this within Chapter 8, A Gentle Introduction to Model Performance Evaluation). Knowing which are the assumptions behind this popular model...