The parametric hazards regression model is sometimes seen as a restrictive class of models by practitioners, and the Cox proportional hazards regression is sometimes preferred over its parametric counterpart. Compared with the parametric models, the interpretation is sometimes lost, and the regular practitioner finds it difficult to connect with the hazards regression model. Of course, an alternative is to build a survival tree over the pseudo observations. Such an attempt can be seen in Tattar's (2016) unpublished paper. Gordon and Olshen (1985) made the first attempt to build a survival tree and many scientists have continued constructing it. LeBlanc and Crowley (1992) are among the most important contributors to set up a survival tree. Zhang and Singer (2010) have also given a systematic development of related methods, and chapters 7-10 of their book deal with survival trees. The basic premise remains the same, and we need good splitting criteria in order to create the survival...
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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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