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 R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required ‚Äì this book will take you methodically through every stage of applying machine learning.

Arrow left icon
Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782162148
Length 396 pages
Edition 1st Edition
Languages
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 (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

Example – identifying poisonous mushrooms with rule learners


Each year, many people fall ill and sometimes even die from ingesting poisonous, wild mushrooms. Since many mushrooms are very similar to each other in appearance, occasionally even experienced mushroom gatherers are poisoned.

Unlike the identification of harmful plants such as a poison oak or poison ivy, there are no clear rules like "leaves of three, let them be" for identifying whether a wild mushroom is poisonous or edible. Complicating matters, many traditional rules such as "poisonous mushrooms are brightly colored" provide dangerous or misleading information. If simple, clear, and consistent rules were available for identifying poisonous mushrooms, they could save the lives of foragers.

As one of the strengths of rule-learning algorithms is the fact that they generate easy to understand rules, they seem like an appropriate fit for this classification task. However, the rules will only be as useful as they are accurate.

Step...

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