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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

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Volume 6, Issue 1

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Volume 1 (2002)

Super Learner

Mark J. van der Laan / Eric C Polley / Alan E. Hubbard
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

About the article

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.

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[130]
Paul Chaffee and Mark van der Laan
Journal of the American Statistical Association, 2012, Volume 107, Number 498, Page 513
[131]
Russell T. Shinohara, Constantine E. Frangakis, and Constantine G. Lyketsos
Biometrics, 2012, Volume 68, Number 1, Page 85
[132]
Iván Díaz Muñoz and Mark van der Laan
Biometrics, 2012, Volume 68, Number 2, Page 541
[133]
T.G. Doeswijk, A.K. Smilde, J.A. Hageman, J.A. Westerhuis, and F.A. van Eeuwijk
Analytica Chimica Acta, 2011, Volume 705, Number 1-2, Page 41
[134]
Hui Wang, Sherri Rose, and Mark J. van der Laan
Statistics & Probability Letters, 2011, Volume 81, Number 7, Page 792

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