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R Data Mining

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

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Length 442 pages
Edition 1st Edition
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Concepts
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Author (1):
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Andrea Cirillo Andrea Cirillo
Author Profile Icon Andrea Cirillo
Andrea Cirillo
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Table of Contents (16) Chapters Close

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

Support vector machines

It is now time to move on to support vector machines, to see if they help us better define the profile of those customers more inclined to get into default status.

First of all, you should notice that support vector machines are way more recent models since they where developed around the 1990s. Secondly, you should notice that when we talk about SVMs, we are actually talking about a family of models rather than a single one.

I am going to show you now just what you need to know to understand the main concepts behind this model, but I will point out to you some good references in case you want to deepen your knowledge of the topic.

The intuition behind support vector machines

There are three main concepts...

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