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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

Product type Paperback

Published in Dec 2014

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Dan Toomey

View More author details

Table of Contents (14) Chapters

Preface

1. Data Mining Patterns

2. Data Mining Sequences FREE CHAPTER

3. Text Mining

4. Data Analysis – Regression Analysis

5. Data Analysis – Correlation

6. Data Analysis – Clustering

7. Data Visualization – R Graphics

8. Data Visualization – Plotting

9. Data Visualization – 3D

10. Machine Learning in Action

11. Predicting Events with Machine Learning

12. Supervised and Unsupervised Learning

Index

Questions

Factual

I have used the sum of squares differences for a crude comparison of models. Do you think this is a fair method?
What cutoff percentage accuracy would you use for your modeling?

When, how, and why?

There are several methods to measure the performance of a model. Investigate which you prefer.
Which modeling technique appears to fit your data?

Challenges

Determine a better way to determine whether a binary data model is accurate against a simple percentage correct.
What kind of data would be more suitable to developing a neural net?
Work with the method argument predict to use other modeling techniques for your data.

The rest of the chapter is locked

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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