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Statistical Applications in Genetics and Molecular Biology

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Super Learner

Mark J. van der Laan
  • University of California, Berkeley
/ Eric C Polley
  • University of California, Berkeley
/ Alan E. Hubbard
  • University of California, Berkeley
Published Online: 2007-09-16 | DOI: https://doi.org/10.2202/1544-6115.1309

When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function.

Keywords: cross-validation; loss-based estimation; machine learning; prediction

Published Online: 2007-09-16

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 6, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1309, September 2007

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