What is Out-of-Bag Error?

Data Science, AI and ML
#1

Out-of-Bag is equivalent to validation or test data . In random forests, there is no need for a separate testing dataset to validate the result. It is calculated internally, during the algorithm run, in the following manner –

As the forest is built on training data, each tree is tested on 1/3rd of the samples (36.8%) that are not used in building that tree (similar to the validation data set).

This is known as the out-of-bag error estimate which in short is an internal error estimate of a random forest as it is being constructed.