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Data Analysis with R

You're reading from  Data Analysis with R

Product type Book
Published in Dec 2015
Publisher
ISBN-13 9781785288142
Pages 388 pages
Edition 1st Edition
Languages
Toc

Table of Contents (20) Chapters close

Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. RefresheR 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. Predicting Continuous Variables 9. Predicting Categorical Variables 10. Sources of Data 11. Dealing with Messy Data 12. Dealing with Large Data 13. Reproducibility and Best Practices Index

Choosing a classifier


These are just four of the most popular classifiers out there, but there are many more to choose from. Although some classification mechanisms perform better on some types of datasets than others, it can be hard to develop an intuition for exactly the ones they are suitable for. In order to help with this, we will be examining the efficacy of our four classifiers on four different two-dimensional made-up datasets—each with a vastly different optimal decision boundary. In doing so, we will learn more about the characteristics of each classifier and have a better sense of the kinds of data they might be better suited for.

The four datasets are depicted in Figure 9.11:

Figure 9.11: A plot depicting the class patterns of our four illustrative and contrived datasets

The vertical decision boundary

The first contrived dataset we will be looking at is the one on the top-left panel of Figure 9.11. This is a relatively simple classification problem, because, just by visual inspection...

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