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Data Analysis with R, Second Edition - Second Edition

You're reading from Data Analysis with R, Second Edition - Second Edition

Product type Book

Published in Mar 2018

Publisher Packt

ISBN-13 9781788393720

Pages 570 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (24) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

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. 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

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Index

Exercises

Practise the following exercises to get a firm grasp on the concepts learned so far:

Did you notice that I put CV in italics when I said that using k=27 seems like a safe bet as measured by the minimization of the CV error? Did you wonder why? I (quite deliberately) made a gaffe in choosing the k in the k-NN from Figure 10.4. My choice wasn't wrong, per se, but my choice of k may have been informed by data that should have been unavailable to me. How might have I committed a common but serious error in hyper-parameter tuning? How might I have done things differently?

Remember that we spent a long time talking about the assumptions of linear regression? In contrast, we spent virtually no time discussing the assumptions of logistic regression. Although logistic regression has less stringent assumptions than its cousin, it is not assumption-free. Think about what some assumptions of logistic regression might be. Confirm your suspicions by doing research on the web. My omission of the...

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