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
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Arrow left icon
Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
Arrow right icon
View More author details
Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning 2. Data Cleaning and Pre-processing FREE CHAPTER 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

Handling Categorical Variables

Categorical variables are a list of string values or numeric values for an attribute. For instance, gender can be "Male" or "Female". There are two types of categories: nominal and ordinal. In nominal categorical data, there is no ordering among the values in that attribute. This is the case with gender values. Ordinal categories have some order within the set of values. For instance, for temperature "Low," "Medium," and "High" have an order.

  • Label Encoding: String literals needs to be converted to numeric values, where "Male" can take value 1 and "Female" can take value 2. This is called integer encoding or label encoding. The integer values have a natural ordering so this may be suitable in cases dealing with categorical data, which is ordinal.
  • One-Hot Encoding: For nominal categories, label encoding is not suitable as the natural order of the numbers may be learned by the machine...
lock icon The rest of the chapter is locked
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