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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 ‚Äì 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 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 Index

Chapter 5. Divide and Conquer – Classification Using Decision Trees and Rules

To make a difficult decision, some people weigh their options by making lists of pros and cons for each possibility. Suppose a job seeker was deciding between several offers, some closer or further from home, with various levels of pay and benefits. He or she might create a list with the features of each position. Based on these features, rules can be created to eliminate some options. For instance, "if I have a commute longer than an hour, then I will be unhappy", or "if I make less than $50k, I won't be able to support my family." The difficult decision of predicting future happiness can be reduced to a series of small, but increasingly specific choices.

This chapter covers decision trees and rule learners—two machine learning methods that apply a similar strategy of dividing data into smaller and smaller portions to identify patterns that can be used for prediction. The knowledge is then presented in the form...

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