Out-Of-Bag Errors

Out-of-bag (OOB) error is a measure used in ensemble learning, specifically in random forest algorithms.

Out-of-bag (OOB) error is a measure used in ensemble learning, specifically in random forest algorithms. It estimates the prediction error of the model without the need for a separate validation set. OOB error is computed by evaluating the model's performance on the training data points that were not included in the construction of each individual decision tree in the forest.