Synonyms
Definition
Stacking is an ensemble learning technique. A set of models are constructed from bootstrap samples of a dataset, then their outputs on a hold-out dataset are used as input to a “meta”-model. The set of base models are called level-0, and the meta-model level-1. The task of the level-1 model is to combine the set of outputs so as to correctly classify the target, thereby correcting any mistakes made by the level-0 models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
(2017). Stacked Generalization. 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_969
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_969
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