Hands-On Ensemble Learning with R
This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, random forest and boosting, to build faster and more accurate machine learning models.
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Difficulty Level
Intermediate
Completion Time
12h32m
Language
English
About Book
Who Is This Book For?
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.
Hands-On Ensemble Learning with R
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h32m total length
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?
Related Resources
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