Chapter 3, Bagging, generalized the decision tree using the bootstrap principle. Before we embark on a journey with random forests, we will quickly review the history of decision trees and highlight some of their advantages and drawbacks. The invention of decision trees followed through a culmination of papers, and the current form of the trees can be found in detail in Breiman, et al. (1984). Breiman's method is popularly known as Classification and Regression Trees, aka CART. Around the late 1970s and early 1980s, Quinlan invented an algorithm called C4.5 independently of Breiman. For more information, see Quinlan (1984). To a large extent, the current form of decision trees, bagging, and random forests is owed to Breiman. A somewhat similar approach is also available in an algorithm popularly known by the abbreviation CHAID, which stands for Chi-square Automatic Interaction Detector. An in-depth look at CART can be found in Hastie, et al. (2009), and a statistical perspective...
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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Current Title:
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