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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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Tools
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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 1. Gearing Up for Predictive Modeling

In this first chapter, we'll start by establishing a common language for models and taking a deep view of the predictive modeling process. Much of predictive modeling involves the key concepts of statistics and machine learning, and this chapter will provide a brief tour of the core distinctions of these fields that are essential knowledge for a predictive modeler. In particular, we'll emphasize the importance of knowing how to evaluate a model that is appropriate to the type of problem we are trying to solve. Finally, we will showcase our first model, the k-nearest neighbors model, as well as caret, a very useful R package for predictive modelers.

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