Out-of-bag evaluation (OOB evaluation): Revision history

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18 March 2023

  • curprev 19:0319:03, 18 March 2023‎ Walle talk contribs‎ 3,571 bytes +3,571‎ 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..."
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