Many ensemble learning methods use a statistical technique called bootstrap sampling. A bootstrap sample of a dataset is another dataset that's obtained by randomly sampling the observations from the original dataset with replacement.
This technique is heavily used in statistics, for example; it is used for estimating standard errors on sample statistics like mean or standard deviation of values.
Let's understand this technique more by taking a look at the following diagram:
Let's assume that we have a population of 1 to 10, which can be considered original population data. To get a bootstrap sample, we need to draw 10 samples ...
