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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

Measuring performance in regression models

Let's make it more technical and discuss the most common metrics available when dealing with regression models. First of all, let's recall what a regression is: we are trying to explain here a response variable with a set of explanatory variables. A reasonable model performance metric will therefore be one that summarizes how well our model is able to explain the explanatory variable itself.

It is no surprise that the most popular regression model metrics are both able to explain this:

Mean squared error
R-squared

Both of them are based on the concept of error, which we already encountered when dealing with model coefficient estimation. We defined as error for a given record of the estimation dataset the difference between the actual value of the response variable and the value of the response value we estimate with our model: