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Data Analysis with R, Second Edition

You're reading from   Data Analysis with R, Second Edition A comprehensive guide to manipulating, analyzing, and visualizing data in R

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788393720
Length 570 pages
Edition 2nd Edition
Languages
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Toc

Table of Contents (24) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. RefresheR FREE CHAPTER 2. The Shape of Data 3. Describing Relationships 4. Probability 5. Using Data To Reason About The World 6. Testing Hypotheses 7. Bayesian Methods 8. The Bootstrap 9. Predicting Continuous Variables 10. Predicting Categorical Variables 11. Predicting Changes with Time 12. Sources of Data 13. Dealing with Missing Data 14. Dealing with Messy Data 15. Dealing with Large Data 16. Working with Popular R Packages 17. Reproducibility and Best Practices 1. Other Books You May Enjoy Index

Chapter 10. Predicting Categorical Variables

Our first foray into predictive analytics began with regression techniques for predicting continuous variables. In this chapter, we will be discussing a perhaps even more popular class of techniques from statistical learning known as classification.

All these techniques have at least one thing in common: we train a learner on input, for which the correct classifications are known, with the intention of using the trained model on new data whose class is unknown. In this way, classification is a set of algorithms and methods to predict categorical variables.

Whether you know it or not, statistical learning algorithms performing classification are all around you. For example, if you've ever accidently checked the spam folder of your email and been horrified, you can thank your lucky stars that there are sophisticated classification mechanisms that your email is run through to automatically mark spam as such so you don't have to see it. On the other...

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