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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

Creating a Package

"If you want something you never had, you have to do something you've never done."
– Thomas Jefferson

We are going to conclude this book by going through the process to create your own R package. If you are doing so, I recommend you start. I've put most of the functionality of data preparation from Chapter 1, Preparing and Understanding Data, into my own package. I'm not planning on putting it on CRAN, nor any package for that matter, with probably the exception of a package of American Civil War data like the Gettysburg file. So why bother with creating a package? If you are going to create code and put it into production, why would you not create a package with version control, examples, and other features? Plus, with RStudio, it is easy to do. So, we will use a simple example with one small function to show how easy it is.

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