Walle
Created page with "{{see also|Machine learning terms}} ==Out-of-Bag Evaluation== Out-of-Bag (OOB) evaluation is a model validation technique commonly used in ensemble learning methods, particularly in bagging algorithms such as Random Forests. The main idea behind OOB evaluation is to use a portion of the training data that was not used during the construction of individual base learners, for the purpose of estimating the performance of the ensemble without resorting to a separ..."
