• repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
• example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
• most common are: cross-validation and the bootstrap
• cross-validation: random sampling with no replacement
• bootstrap: random sampling with replacement
• cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
• bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

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