The bootstrap is a general tool for assessing statistical accuracy. The basic
idea is to randomly draw datasets with replacement from the training data,
each samples the same size as the original training set. This is done many
times (say k = 100), producing k bootstrap datasets. Then we refit the model
to each of the bootstrap datasets and examine the behavior of the fits over
the k replications.