Book Image

Hands-On Ensemble Learning with R

By : Prabhanjan Narayanachar Tattar
Book Image

Hands-On Ensemble Learning with R

By: Prabhanjan Narayanachar Tattar

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.
Table of Contents (17 chapters)
Hands-On Ensemble Learning with R
Contributors
Preface
12
What's Next?
Index

Summary


The main purpose of dealing with the bootstrap method in detail is that it lays the foundation for the resampling methods. We began the chapter with a very early resampling method: the jackknife method. This method is illustrated for the purpose of multiple scenarios, including survival data, which is inherently complex. The bootstrap method kicked off for seemingly simpler problems, and then we immediately applied it to complex problems, such as principal components and regression data. For the regression data, we also illustrated the bootstrap method for survival data and time series data. In the next chapter, we will look at the central role the bootstrap method plays in resampling decision trees, a quintessential machine learning tool.