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Background and objective: Stacking is an ensemble machine learning methodthat averages predictions from multiple other algorithms, such as generalizedlinear models and regression trees. An implementation of stacking, called superlearning, has been developed as a general approach to supervised learning andhas seen frequent usage, in part due to the availability of an R package. Wedevelop super learning in the SAS software system using a new macro, anddemonstrate its performance relative to the R package.  Methods: Following previous work using the R SuperLearner package we assessthe performance of super learning in a number of domains. We compare the Rpackage with the new SAS macro in a small set of simulations assessing curvefitting in a predictive model as well in a set of 14 publicly availabledatasets to assess cross-validated accuracy.  Results: Across the simulated data and the publicly available data, the SASmacro performed similarly to the R package, despite a different set ofpotential algorithms available natively in R and SAS.  Conclusions: Our super learner macro performs as well as the R package at anumber of tasks. Further, by extending the macro to include the use of Rpackages, the macro can leverage both the robust, enterprise orientedprocedures in SAS and the nimble, cutting edge packages in R. In the spirit ofensemble learning, this macro extends the potential library of algorithmsbeyond a single software system and provides a simple avenue into machinelearning in SAS.

Super learning in the SAS system