Out Of Bag Evaluation(OOB) And OOB Score Or Error In Random Forest

Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling with replacement to create training samples for the model to learn from. OOB error is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample
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[@michaelho5138]

How can we be sure that after bootstrapping of the data, there will be remaining out of bag records to be used as a validating training data set? Is it not possible that you could sample all data points even with replacement when bootstrapping?

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