Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Hands-On Ensemble Learning with R

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

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788624145

Length 376 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Ensemble Techniques FREE CHAPTER

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?

A. Bibliography

R package references

Index

R package references

Prabhanjan Tattar (2015). ACSWR: A Companion Package for the Book "A

Course in Statistics with R". R package version 1.0.

https://CRAN.R-project.org/package=ACSWR

Alfaro, E., Gamez, M. Garcia, N.(2013). adabag: An R Package for

Classification with Boosting and Bagging. Journal of Statistical

Software, 54(2), 1-35. URL http://www.jstatsoft.org/v54/i02/.

Angelo Canty and Brian Ripley (2017). boot: Bootstrap R (S-Plus)

Functions. R package version 1.3-19.

John Fox and Sanford Weisberg (2011). An {R} Companion to Applied

Regression, Second Edition. Thousand Oaks CA: Sage. URL:

http://socserv.socsci.mcmaster.ca/jfox/Books/Companion car

Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams,

Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton

Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew

Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2017).

caret: Classification and Regression Training. R package...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (1)

Tattar

Tattar

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.

See other products by Tattar

Other recommended products

Related to this chapter

Machine Learning with R Cookbook

Machine Learning with R Cookbook

The R language is a powerful open source functional programming language. At its core, R is a statistical language that provides impressive tools to analyze data and create high-level graphics. This book covers the basics of R by setting up a user-friendly programming environment and programming ETL in R. Data exploration examples are provided that demonstrate how powerful data visualisation and machine learning is in discovering hidden relationships. You will also explore air quality data, steps to fix the missing values and visualising the same. You will then dive into important machine learning topics, including data classification, regression, survival analysis, time series analysis, clustering association rule mining, and dimension reduction.This book will include the latest code and examples based on R 3.3 and above—updated for better computation, accuracy, and speed with R.

Oct 2017 19h 4m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Statistical Application Development with R and Python

Statistical Application Development with R and Python

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

Aug 2017 14h 24m

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns.

May 2019 8h 52m

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns.

May 2019 8h 52m

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns.

May 2019 8h 52m

Ensemble Machine Learning Cookbook

Ensemble Machine Learning Cookbook

This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference to implement a wide range of tasks and solve the common and uncommon problems in ensemble machine learning domain.

Jan 2019 11h 12m

Practical Data Science Cookbook

Practical Data Science Cookbook

As an increasing amount of data is generated each year, and the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data have a competitive advantage over companies that don't. This drives a higher demand for knowledgeable and competent data professionals. By sequentially working through the steps presented in each chapter, you will quickly familiarize yourself with the data science process, and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis - R and Python.

Jun 2017 14h 28m

Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relationship between each data point in the series.

May 2019 14h 56m

Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relationship between each data point in the series.

May 2019 14h 56m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m