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Hands-On Ensemble Learning with R

Hands-On Ensemble Learning with R

By : Tattar
3 (1)
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Hands-On Ensemble Learning with R

Hands-On Ensemble Learning with R

3 (1)
By: Tattar

Overview of this book

Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
Table of Contents (15 chapters)
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12
12. What's Next?
13
A. Bibliography
14
Index

To get the most out of this book

  1. The official website of R is the Comprehensive R Archive Network (CRAN) at www.cran.r-project.org. At the time of writing this book, the most recent version of R is 3.5.1. This software is available for three platforms: Linux, macOS, and Windows. The reader can also download a nice frontend, such as RStudio.
  2. Every chapter has a header section titled Technical requirements. It gives a list of R packages required to run the code in that chapter. For example, the requirements for Chapter 3, Bagging, are as follows:
    • class
    • FNN
    • ipred
    • mlbench
    • rpart

The reader then needs to install all of these packages by running the following lines in the R console:

install.packages("class")
install.packages("mlbench")
install.packages("FNN")
install.packages("rpart")
install.packages("ipred")

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/Hands-On-Ensemble-Learning-with-R. In case there's an update to the code, it will be updated on the existing GitHub repository.

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: http://www.packtpub.com/sites/default/files/downloads/HandsOnEnsembleLearningwithR_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; "The computation of the values of the density functions using the dexp function."

A block of code is set as follows:

> Events_Prob <- apply(Elements_Prob,1,prod)
> Majority_Events <- (rowSums(APC)>NT/2)
> sum(Events_Prob*Majority_Events)
[1] 0.9112646

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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