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Machine Learning with R

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

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
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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 (16) Chapters Close

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 Other Books You May Enjoy
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Index

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

When deciding between job offers with various levels of pay and benefits, many people begin by making lists of pros and cons, then eliminate options using simple rules. For instance, saying "If I must commute more than an hour, I will be unhappy," or "If I make less than $50K, I won't be able to support my family." In this way, the complex and difficult decision of predicting one's future happiness can be reduced to a series of simple decisions.

This chapter covers decision trees and rule learners—two machine learning methods that also make complex decisions from sets of simple choices. These methods present their knowledge in the form of logical structures that can be understood with no statistical knowledge. This aspect makes these models particularly useful for business strategy and process improvement.

By the end of this chapter, you will learn...

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