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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
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Author (1):
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Tony Fischetti Tony Fischetti
Author Profile Icon Tony Fischetti
Tony Fischetti
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

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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