title: Super learner.
abstract: 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.
authors.affiliation: University of California, Berkeley, USA. laan@stat.berkeley.edu
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
authors.forename: Eric C
authors.initials: EC
authors.lastname: Polley
authors.forename: Alan E
authors.initials: AE
authors.lastname: Hubbard
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015233:Models, Statistical