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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Toc

Table of Contents (14) Chapters Close

Questions

Factual

  • Which supervised learning technique(s) do you lean towards as your "go to" solution?
  • Why are the density plots for Bayesian results off-center?

When, how, and why?

  • How would you decide on the number of clusters to use?
  • Find a good rule of thumb to decide the number of hidden layers in a neural net.

Challenges

  • Investigate other blind signal separation techniques, such as ICA.
  • Use other methods, such as poisson, in the rpart function (especially if you have a natural occurring dataset).
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