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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

Summary

This is the author speaking here. Did you solve the mystery of the revenues drop? You did not, I guess. Nevertheless, you made some relevant steps on your journey to learning how to use R for data mining activities.

In this chapter, you learned some conceptual and some practical stuff, and you now possess medium-level skills to define and measure the performance of data mining models.

Andy first explained to you what we do intend for model performance and how this concept is related to the one of model interpretability and the purposes for which the model was estimated.

You then learned what the main model metrics are for both regression and classification problems.

Firstly, you were introduced to the relevant concepts of error, mean squared errors, and R-squared.

About this latter statistics, you also carefully analyzed its meaning and the common misconceptions regarding it. I strongly advise you to carefully hold these misconceptions in your mind, since in your everyday professional...