In earlier chapters, ensemble methods were found to be effective. In the previous chapter, we looked at scenarios in which ensemble methods increase the overall accuracy of a prediction. It has previously been assumed that different base learners are independent of each other. However, unless we have a very large sample and the base models are learners that use a distinct set of observations, such an assumption is very impractical. Even if we had a large enough sample to believe that the partitions are nonoverlapping, each base model is built on a different partition, and each partition carries with it the same information as any other partition. However, it is difficult to test validations such as this, so we need to employ various techniques in order to validate the independence of the base models on the same dataset. To do this, we will look at various different methods. A brief discussion of the need for ensemble diagnostics will kick off this chapter...
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