Hands-On Ensemble Learning with R: 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 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.

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Hands-On Ensemble Learning with R: 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 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.

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Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

by Prabhanjan Narayanachar Tattar
Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

by Prabhanjan Narayanachar Tattar

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$39.99 

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Overview

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.


Product Details

ISBN-13: 9781788629171
Publisher: Packt Publishing
Publication date: 07/27/2018
Sold by: Barnes & Noble
Format: eBook
Pages: 376
File size: 14 MB
Note: This product may take a few minutes to download.

About the Author

Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics 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.

Table of Contents

Table of Contents
  1. Introduction to Ensemble Techniques
  2. Bootstrapping
  3. Bagging
  4. Random Forests
  5. The Bare Bones Boosting Algorithms
  6. Boosting Refinements
  7. The General Ensemble Technique
  8. Ensemble Diagnostics
  9. Ensembling Regression Models
  10. Ensembling Survival Models
  11. Ensembling Time Series Models
  12. What's Next?
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