Bootstrapping:

To understand bootstrap, suppose it were possible to draw repeated samples (of the same size) from the population of interest, a large number of times. Then, one would get a fairly good idea about the sampling distribution of a particular statistic from the collection of its values arising from these repeated samples. The idea behind bootstrap is to use the data of a sample study at hand as a “surrogate population”, for the purpose of approximating the sampling distribution of a statistic; i.e. to resample (with replacement) from the sample data at hand and create a large number of “phantom samples” known as bootstrap samples.

In other words, We randomly sample with replacement from the n known observations. We then call this a bootstrap sample. Since we allow for replacement, this bootstrap sample most likely not identical to our initial sample. Some data points may be duplicated, and others data points from the initial may be omitted in a bootstrap sample.

An Example:

The following numerical example will help to demonstrate how the process works. If we begin with the sample 2, 4, 5, 6, 6, then all of the following are possible bootstrap samples:

2 ,5, 5, 6, 6

4, 5, 6, 6, 6

2, 2, 4, 5, 5

2, 2, 2, 4, 6

2, 2, 2, 2, 2

4,6, 6, 6, 6

Bagging:

Bootstrap aggregating (bagging) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method