Book Image

Hands-On Ensemble Learning with R

By : Prabhanjan Narayanachar Tattar
Book Image

Hands-On Ensemble Learning with R

By: Prabhanjan Narayanachar Tattar

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
What's Next?


Survival data is different to typical regression data, and the incomplete observations pose a challenge. Since the data structure is completely different, we need specialized techniques to handle the incomplete observations and to that end, we introduced core survival concepts, such as hazard rate and survival function. We then introduced parametric lifetime models, which gives us a brief peek at how the lifetime distribution should look. We even fitted these lifetime distributions into the pbc dataset.

We also learned that the parametric setup might be very restrictive, and hence considered the nonparametric methods of the estimation of survival quantities. We also demonstrated the utility of the Nelson-Aalen estimator, the Kaplan-Meier survival function, and the log-rank test. The parametric hazards regression model was backed with the Cox proportional hazards regression model and applied to the pbc dataset. The logrank test can also help in the splitting criteria, and it has also...