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Estimating a prediction function is a fundamental component of many dataanalyses. The Super Learner ensemble, a particular implementation of stacking,has desirable theoretical properties and has been used successfully in manyapplications. Dimension reduction can be accomplished by using variablescreening algorithms, including the lasso, within the ensemble prior to fittingother prediction algorithms. However, the performance of a Super Learner usingthe lasso for dimension reduction has not been fully explored in cases wherethe lasso is known to perform poorly. We provide empirical results that suggestthat a diverse set of candidate screening algorithms should be used to protectagainst poor performance of any one screen, similar to the guidance forchoosing a library of prediction algorithms for the Super Learner.

Practical considerations for variable screening in the Super Learner