Current version of the SuperLearner R package
Latest commit a398138 Mar 21, 2017 @ecpolley committed on GitHub Merge pull request #70 from ck37/ranger
New wrapper: ranger alternative random forest implementation

SuperLearner: Prediction model ensembling method

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This is the current version of the SuperLearner R package (version 2.*).


  • Automatic optimal predictor ensembling via cross-validation.
  • Includes dozens of algorithms including Random Forest, GBM, XGBoost, BART, Elastic Net, and Neural Networks.
  • Integrates with caret to support even more algorithms.
  • Includes framework to quickly add custom algorithms to the ensemble
  • Visualize the performance of each algorithm using built-in plotting.
  • Easily incorporate multiple hyperparameter configurations for each algorithm into the ensemble.
  • Add new algorithms or change the default parameters for existing ones.
  • Screen variables (feature selection) based on univariate association, Random Forest, Elastic Net, et al. or a custom screening algorithms.
  • Multi-core and multi-node parallelization for scalability.
  • External cross-validation to estimate the performance of the ensembling predictor.
  • Ensemble can optimize for any target metric: mean-squared error, AUC, log likelihood, etc.
  • Includes framework to provide custom loss functions and stacking algorithms

Install the development version from GitHub:

if (!require("devtools")) install.packages("devtools")

Install the current release from CRAN:



Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226.

van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25.

van der Laan, M. J., & Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media.