= Out-of-bag error
{wiki=Out-of-bag_error}
Out-of-bag (OOB) error is a concept primarily used in the context of ensemble machine learning methods, particularly with bootstrap aggregating, or bagging, approaches like Random Forests. It provides a way to estimate the generalization error of a model without the need for a separate validation dataset. Here's how it works: 1. **Bootstrap Sampling**: In a bagging algorithm, multiple subsets of the training data are created by randomly sampling with replacement.
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