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Machine Learning with R

You're reading from   Machine Learning with R R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782162148
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Table of Contents (19) Chapters Close

Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing Machine Learning FREE CHAPTER 2. Managing and Understanding Data 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Index

Vectors


The fundamental R data structure is the vector, which stores an ordered set of values called elements. A vector can contain any number of elements. However, all the elements must be of the same type; for instance, a vector cannot contain both numbers and text.

There are several vector types commonly used in machine learning: integer (numbers without decimals), numeric (numbers with decimals), character (text data), or logical (TRUE or FALSE values). There are also two special values: NULL, which is used to indicate the absence of any value, and NA, which indicates a missing value.

It is tedious to enter large amounts of data manually, but simple vectors can be created by using the combine function c(). The vector can also be given a name using the arrow <- operator, which is R's assignment operator, used in a similar way to the = assignment operator in many other programming languages.

For example, let's construct a set of vectors containing data on three medical patients. We'll...

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