The previous four chapters have dealt with the ensembling techniques for decision trees. In each of the topics discussed in those chapters, the base learner was a decision tree and, consequently, we delved into the homogenous ensembling technique. In this chapter, we will demonstrate that the base learner can be any statistical or machine learning technique and their ensemble will lead to improved precision in predictions. An important requirement will be that the base learner should be better than a random guess. Through R programs, we will discuss and clarify the different possible cases in which ensembling will work. Voting is an important trait of the classifiers – we will state two different methods for this and illustrate them in the context of bagging and random forest ensemblers. The averaging technique is an ensembler for regression variables, which will follow the discussion of classification methods. The chapter will conclude with a detailed...
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