Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

Arrow left icon
Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 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 Other Books You May Enjoy
Leave a review - let other readers know what you think
Index

Managing data with R

One of the challenges faced while working with massive datasets involves gathering, preparing, and otherwise managing data from a variety of sources. Although we will cover data preparation, data cleaning, and data management in depth by working on real-world machine learning tasks in later chapters, this section highlights the basic functionality for getting data in and out of R.

Saving, loading, and removing R data structures

When you have spent a lot of time getting a data frame into the desired form, you shouldn't need to recreate your work each time you restart your R session. To save a data structure to a file that can be reloaded later or transferred to another system, use the save() function. The save() function writes one or more R data structures to the location specified by the file parameter. R data files have an .RData extension.

Suppose you had three objects named x, y, and z that you would like to save to a permanent file. Regardless of whether...

You have been reading a chapter from
Machine Learning with R - Third Edition
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781788295864
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image