In the first section, we looked at the role of pseudovalues in carrying out inference related to survival data. The main idea behind the use of pseudovalues is to replace the incomplete observations with an appropriate (expected) value and then use the flexible framework of the generalized estimating equation. Survival analysis and the related specialized methods for it will be detailed in Chapter 10, Ensembling Survival Models, of the book. We will briefly introduce the notation here as required to set up the parameters. Let T denote the survival time, or the time to the event of interest, and we naturally have , which is a continuous random variable. Suppose that the lifetime cumulative distribution is F and the associated density function is f. Since the lifetimes T are incomplete for some of the observations and subject to censoring, we will not be able to properly infer about interesting parameters such as mean survival time or median survival time. Since...
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.
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