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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Toc

Table of Contents (16) Chapters Close

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 Other Books You May Enjoy
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Index

What you need for this book

The examples in this book were written for and tested with R version 3.5.2 on Microsoft Windows and Mac OS X, though they are likely to work with any recent version of R.

Download the example code files

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

  1. Log in or register at http://www.packtpub.com.
  2. Select the SUPPORT tab.
  3. Click on Code Downloads & Errata.
  4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

  • WinRAR/7-Zip for Windows
  • Zipeg/iZip/UnRarX for Mac
  • 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-with-R-Third-Edition, and at https://github.com/dataspelunking/MLwR/. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781788295864_ColorImages.pdf.

Conventions used

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code in text, function names, filenames, file extensions, user input, and R package names are shown as follows: "The knn() function in the class package provides a standard, classic implementation of the k-NN algorithm."

R user input and output is written as follows:

> table(mushrooms$type)

   edible poisonous 
     4208      3916

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The Task Views link on the left side of the CRAN page provides a curated list of packages."

Note

Important notes appear like this.

Tip

Tips and tricks appear like this.

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