AI engineering glossary · Classical ML algorithms
What is bootstrap sampling?
Bootstrap sampling: Creating a new dataset by drawing rows at random from the original one with replacement, so some rows repeat and others are left out. A sample of size n contains about 63% of the distinct original rows. It underlies bagging and uncertainty estimation.
Bootstrap sampling: an example
From 5 rows [A,B,C,D,E], one bootstrap sample could be [B,B,D,A,E], leaving C out.
Related terms
More Classical ML algorithms terms
Where you learn it
Bootstrap sampling is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.