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European Journal of Epidemiology

, Volume 33, Issue 5, pp 459–464 | Cite as

Stacked generalization: an introduction to super learning

  • Ashley I. Naimi
  • Laura B. Balzer
METHODS

Abstract

Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into a host of methods among which is the “Super Learner”. Super Learner uses V-fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details. We work step-by-step through two examples to illustrate concepts and address common concerns.

Keywords

Ensemble learning Machine learning Super Learner Stacked generalization Stacked regression 

Notes

Acknowledgements

We thank Susan Gruber and Mark J van der Laan for expert advice.

Funding

NIH Grant Number UL1TR001857 and R37AI051164.

Compliance with ethical standards

Conflicts of interest

The author declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EpidemiologyUniversity of PittsburghPittsburghUSA
  2. 2.Department of Biostatistics and EpidemiologyUniversity of MassachusettsAmherstUSA

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