  Estimating a prediction function is a fundamental component of many data
analyses. The Super Learner ensemble, a particular implementation of stacking,
has desirable theoretical properties and has been used successfully in many
applications. Dimension reduction can be accomplished by using variable
screening algorithms, including the lasso, within the ensemble prior to fitting
other prediction algorithms. However, the performance of a Super Learner using
the lasso for dimension reduction has not been fully explored in cases where
the lasso is known to perform poorly. We provide empirical results that suggest
that a diverse set of candidate screening algorithms should be used to protect
against poor performance of any one screen, similar to the guidance for
choosing a library of prediction algorithms for the Super Learner.
