Bootstrap resampling is a way to create new samples of the original data. It obtains new datasets by sampling observations from the original set through replacement. These new datasets have the same sample size as the original and are useful in determining estimates for the standard error and confidence intervals of a statistic by comparing the accuracy of the model for each new data set.

Bootstrap method for resampling

Divides the training set into two parts - a training set and a validation set (also known as the “holdout set”). A model is made to fit the training set, and then the validation set is used to test the accuracy of the model because the model is blind to it. This gives an estimate of the test error of the model and can be useful in determining the best model from a series of models using different hyperparameters.

Cross-Validation for resampling

University of Michigan - Ann Arbor

To learn more about a data set and fitted model we can use resampling methods to repeatedly draw samples from the training set to build new models in order to estimate the fitted models uncertainty (can yeild estimates for standard errors and confidence limits).

Resampling in supervised statistical learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

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