Definition
Bootstrap sampling is a process for creating a distribution of datasets out of a single dataset. It is used in the ensemble learning algorithm Bagging. It can also be used in algorithm evaluation to create a distribution of training sets from which to estimate properties of an algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Davison AC, Hinkley D (2006) Bootstrap methods and their applications, 8th edn. Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
(2017). Bootstrap Sampling. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_977
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_977
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering