The GC_Random_Forest.pdf file consists of the 500 trees which serve as the homogeneous learners in the random forest ensemble. It is well known that a decision tree has a nice and clear interpretation. This is because it shows how one traverses the path to a terminal node. The random selection of features at each split and the bootstrap samples lead to the setting up of the random forest. Refer to the figure Trees of the Random Forest, which depicts trees numbered 78, 176, 395, and 471. The first split across the four trees is respectively purpose, amount, property, and duration. The second split for the first left side of these four trees is employed, resident, purpose, and amount, respectively. It is a cumbersome exercise to see which variables are meaningful over the others. We know that the earlier a variable appears, the higher its importance is. The question that then arises is, with respect to a random forest, how do we find the depth distribution of the variables...
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