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Mastering Machine Learning with R

You're reading from   Mastering Machine Learning with R Advanced machine learning techniques for building smart applications with R 3.5

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Product type Paperback
Published in Jan 2019
Publisher
ISBN-13 9781789618006
Length 354 pages
Edition 3rd Edition
Languages
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Author (1):
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Cory Lesmeister Cory Lesmeister
Author Profile Icon Cory Lesmeister
Cory Lesmeister
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Table of Contents (16) Chapters Close

Preface 1. Preparing and Understanding Data 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Advanced Feature Selection in Linear Models 5. K-Nearest Neighbors and Support Vector Machines 6. Tree-Based Classification 7. Neural Networks and Deep Learning 8. Creating Ensembles and Multiclass Methods 9. Cluster Analysis 10. Principal Component Analysis 11. Association Analysis 12. Time Series and Causality 13. Text Mining 14. Creating a Package 15. Other Books You May Enjoy

Datasets and modeling

We're going to be using two of the prior datasets, the simulated data from Chapter 4, Advanced Feature Selection in Linear Models, and the customer satisfaction data from Chapter 3, Logistic Regression. We'll start by building a classification tree on the simulated data. This will help us to understand the basic principles of tree-based methods. Then, we'll move on to random forest and boosted trees applied to the customer satisfaction data. This exercise will provide an excellent comparison to the generalized linear models from before. Finally, I want to show you an interesting feature selection method using random forest, using the simulated data. By interesting, I mean it's a valuable technique to add to your feature selection arsenal, but I'll point out a couple of caveats for you to consider in practical application.

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