#### 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.
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

## Core concepts of survival analysis

Survival analysis deals with censored data, and it is very common that parametric models are unsuitable for explaining the lifetimes observed in clinical trials.

Let T denote the survival time, or the time to the event of interest, and we will naturally have , which is a continuous random variable. Suppose that the lifetime cumulative distribution is F and the associated density function is f. We define important concepts as required for further analysis. We will explore the concept of survival function next.

Suppose that T is the continuous random variable of a lifetime and that the associated cumulative distribution function is F. The survival function at time t is the probability the observation is still alive at the time, and it is defined by the following:

The survival function can take different forms. Let's go through some examples for each of the distributions to get a clearer picture of the difference in survival functions.

Exponential Distribution...