Glossary term
Glossary term
Foundations
A method of picking items from a set of candidate items in which the same item can be picked multiple times. The phrase "with replacement" means that after each selection, the selected item is returned to the pool of candidate items. The inverse method, sampling without replacement, means that a candidate item can only be picked once.
For example, consider the following fruit set:
fruit = {kiwi, apple, pear, fig, cherry, lime, mango}
Suppose that the system randomly picks fig as the first item. If using sampling with replacement, then the system picks the second item from the following set:
fruit = {kiwi, apple, pear, fig, cherry, lime, mango}
Yes, that's the same set as before, so the system could potentially pick fig again.
If using sampling without replacement, once picked, a sample can't be picked again. For example, if the system randomly picks fig as the first sample, then fig can't be picked again. Therefore, the system picks the second sample from the following (reduced) set:
fruit = {kiwi, apple, pear, cherry, lime, mango}
Click the icon for additional notes.
For example, consider the following fruit set:
fruit = {kiwi, apple, pear, fig, cherry, lime, mango}
Suppose that the system randomly picks fig as the first item. If using sampling with replacement, then the system picks the second item from the following set:
Created for this library
A research team uses sampling with replacement in bootstrap analysis to estimate confidence intervals for model metrics.
A risk modeling team uses sampling with replacement in bagging to train each tree in a random forest on a bootstrapped sample.
A retail analytics team uses sampling with replacement to estimate the variability of model accuracy on small held-out samples.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License