
Hands-On Ensemble Learning with R
A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
Prabhanjan Narayanachar Tattar(Author)
Packt Publishing
Published on 27. July 2018
Book
Paperback/Softback
376 pages
978-1-78862-414-5 (ISBN)
Description
Explore powerful R packages to create predictive models using ensemble methods
Key Features
Implement machine learning algorithms to build ensemble-efficient models
Explore powerful R packages to create predictive models using ensemble methods
Learn to build ensemble models on large datasets using a practical approach
Book DescriptionEnsemble 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.
What you will learn
Carry out an essential review of re-sampling methods, bootstrap, and jackknife
Explore the key ensemble methods: bagging, random forests, and boosting
Use multiple algorithms to make strong predictive models
Enjoy a comprehensive treatment of boosting methods
Supplement methods with statistical tests, such as ROC
Walk through data structures in classification, regression, survival, and time series data
Use the supplied R code to implement ensemble methods
Learn stacking method to combine heterogeneous machine learning models
Who this book is forThis 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.
Key Features
Implement machine learning algorithms to build ensemble-efficient models
Explore powerful R packages to create predictive models using ensemble methods
Learn to build ensemble models on large datasets using a practical approach
Book DescriptionEnsemble 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.
What you will learn
Carry out an essential review of re-sampling methods, bootstrap, and jackknife
Explore the key ensemble methods: bagging, random forests, and boosting
Use multiple algorithms to make strong predictive models
Enjoy a comprehensive treatment of boosting methods
Supplement methods with statistical tests, such as ROC
Walk through data structures in classification, regression, survival, and time series data
Use the supplied R code to implement ensemble methods
Learn stacking method to combine heterogeneous machine learning models
Who this book is forThis 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 20 mm
Weight
701 gr
ISBN-13
978-1-78862-414-5 (9781788624145)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Prabhanjan Narayanachar Tattar
Hands-On Ensemble Learning with R
A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
E-Book
09/2024
1st Edition
Packt Publishing Limited
€34.99
Available for download
Person
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.
Content
Table of Contents
Introduction to Ensemble Techniques
Bootstrapping
Bagging
Random Forests
The Bare Bones Boosting Algorithms
Boosting Refinements
The General Ensemble Technique
Ensemble Diagnostics
Ensembling Regression Models
Ensembling Survival Models
Ensembling Time Series Models
What's Next?
Introduction to Ensemble Techniques
Bootstrapping
Bagging
Random Forests
The Bare Bones Boosting Algorithms
Boosting Refinements
The General Ensemble Technique
Ensemble Diagnostics
Ensembling Regression Models
Ensembling Survival Models
Ensembling Time Series Models
What's Next?