This is the code repository for Hands-On Ensemble Learning with R, published by Packt.
A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a strong model. It delivers superior prediction power and can give your datasets a boost in accuracy.
This book covers the following exciting features:
- Carry out an essential review of re-sampling methods, bootstrap, and model averaging
- Explore coverage of ensemble methods such as bagging, random forests, and boosting
- Use multiple algorithms to make strong predictive models
- Enjoy comprehensive treatment of boosting methods
- Supplement methods with statistical tests such as ROC
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
set.seed(1234)
X <- mvrnorm(n = 200, mu = c(0, 0, 0, 0, 0),
Sigma = matrix(c(
1, .9999, .99, .99, .10,
Following is what you need for this book: This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | Hardware required |
|---|---|---|
| 1 -11 | R version 3.3.0 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & An alytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.
- R Statistical Application Development by Example Beginner's Guide
- Statistical Application Development with R and Python - Second Edition
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