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The bootstrap is a statistical method for estimating the performance (e.g., accuracy) of classification or regression methods. The bootstrap is based on the statistical procedure of sampling with replacement. Unlike other estimation methods such as cross-validation, the same object or tuple can be selected for the training set more than once in the boostrap. That is, each time a tuple is selected, it is equally likely to be selected again and re-added to the training set.
Historical Background
The bootstrap sampling was developed by Bradley Efron in 1979, and mainly used for estimating the statistical parameters such as mean, standard errors, etc. [2]. A meta-classification method using the bootstrap called bootstrap aggregating (or bagging) was proposed by Leo Breiman in 1994 to improve the classification by combining classifications of randomly generated training sets [1].
Foundations
This section discusses a commonly used...
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Recommended Reading
Breiman L. Bagging predictors. Machine Learning; 1996.
Efron B, Tibshirani RJ. An introduction to the bootstrap. Boca Raton: CRC Press; 1994.
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© 2016 Springer Science+Business Media New York
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Yu, H. (2016). Bootstrap. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_566-2
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DOI: https://doi.org/10.1007/978-1-4899-7993-3_566-2
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