Statistical models, say linear regression and logistic regression, indicate which variables are significant with measures such as p-value and t-statistics. In a decision tree, a split is caused by a single variable. If the specification of the number of variables for the surrogate splits, a certain variable may appear as the split criteria more than once in the tree and some variables may never appear in the tree splits at all. During each split, we select the variable that leads to the maximum reduction in impurity, and the contribution of a variable across the tree splits would also be different. The overall improvement across each split of the tree (by the reduction in impurity for the classification tree or by the improvement in the split criterion) is referred to as the variable importance. In the case of ensemble methods such as bagging and random forest, the variable importance is measured for each tree in the technique. While the concept of variable importance...
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