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Mastering Predictive Analytics with R

You're reading from   Mastering Predictive Analytics with R Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781783982806
Length 414 pages
Edition 1st Edition
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Toc

Table of Contents (13) Chapters Close

Preface 1. Gearing Up for Predictive Modeling 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Neural Networks 5. Support Vector Machines 6. Tree-based Methods 7. Ensemble Methods 8. Probabilistic Graphical Models 9. Time Series Analysis 10. Topic Modeling 11. Recommendation Systems Index

Chapter 6. Tree-based Methods

In this chapter, we are going to present one of the most intuitive ways to create a predictive model—using the concept of a tree. Tree-based models, often also known as decision tree models, are successfully used to handle both regression and classification type problems. We'll explore both scenarios in this chapter, and we'll be looking at a range of different algorithms that are effective in training these models. We will also learn about a number of useful properties that these models possess, such as their ability to handle missing data and the fact that they are highly interpretable.

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