Could you describe the bootstrapping technique and its efficacy in augmenting sample size?

Question

Anonymous · Accepted Answer

Bootstrapping is the process of sampling your data with replacement to expand
the number of data points available. This can lead to having a larger data set
because you can sample the data as much as you like. This is because you are
sampling with replacement, so you're putting back in what you took out. While
bootstrapping can help by providing more data, the data itself is the same. If
the sampled data does not sufficiently cover the distribution that you are
covering, or is misrepresentative, then bootstrapping may make this worse. For
example, if you only have milkyways and twix in your halloween candy bucket,
then bootstrapping will never yield you a reese's

Anonymous · Answer

It is a resample technique to help estimate the uncertainty of a statistical
model. From
the original dataset, you derive many other dataset by randomly selecting data
and each data
point can be repeated many times. The desired statistic is calculated for all
new datasets.
● Very useful in scenarios where there is only a small amount of data available;
● Can be used in machine learning to estimate the accuracy of a classifier;
● Random forest uses bootstrapping to train several trees and evaluate
prediction based
on majority of predictions given by the trees, a.k.a, the fores

Could you describe the bootstrapping technique and its efficacy in augmenting sample size?

Answers