Ensemble methods have been found to be very effective for classification, regression, and other related problems. Any statistical and machine learning method must always be followed up with appropriate diagnostics. The assumption that all base models are independent of each other is central to the success of an ensembling method. However, this independence condition is rarely satisfied, especially because the base models are built on the same dataset. We kicked off the chapter with the simplest measure: the geese pair method. With this, we essentially searched for the models that agree with each other at all times. If such models are present in the ensemble, it is safer to remove one of them. With a large dataset and a high number of variables, it is indeed possible that there won't be any base models that speak the same language as another. However, we still need to check whether they are equal. With this in mind, we first proposed measures that compare only two base models at a...
Hands-On Ensemble Learning with R
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Hands-On Ensemble Learning with R
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Overview of this book
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
Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
Contributors
Preface
Free Chapter
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?
Bibliography
Index
Customer Reviews