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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.

Product type Paperback

Published in Oct 2013

Publisher Packt

ISBN-13 9781782162148

Length 396 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Brett Lantz

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Table of Contents (19) Chapters

Machine Learning with R

Credits

About the Author

About the Reviewers

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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

Understanding regression trees and model trees

If you recall from Chapter 5, Divide and Conquer – Classification Using Decision Trees and Rules, a decision tree builds a model much like a flowchart in which decision nodes, leaf nodes, and branches define a series of decisions that can be used to classify examples. Such trees can also be used for numeric prediction by making only small adjustments to the tree growing algorithm. In this section, we will consider only the ways in which trees for numeric prediction differ from trees used for classification.

Trees for numeric prediction fall into two categories. The first, known as regression trees, were introduced in the 1980s as part of the seminal Classification and Regression Tree (CART) algorithm. Despite the name, regression trees do not use linear regression methods as described earlier in this chapter; rather, they make predictions based on the average value of examples that reach a leaf.