Random Forests are unique in this matter because they have a built in dataset that can be used to test accuracy while it is run. This is called the Out-of-Bag Error Estimate (also called O.O.B), and it is a method for testing the accuracy of random forests, boosted decision trees, and other machine learning algorithms.
