SuperLearner: Prediction model ensembling method
This is the current version of the SuperLearner R package (version 2.*).
- Automatic optimal predictor ensembling via cross-validation with one line of code.
- Dozens of algorithms: XGBoost, Random Forest, GBM, Lasso, SVM, BART, KNN, Decision Trees, Neural Networks, and more.
- 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 check multiple hyperparameter configurations for each algorithm in 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 custom screening algorithms.
- Multicore and multinode 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:
# install.packages("remotes") remotes::install_github("ecpolley/SuperLearner")
Install the current release from CRAN:
SuperLearner makes it trivial to run many algorithms and use the best one or an ensemble.
data(Boston, package = "MASS") set.seed(1) sl_lib = c("SL.xgboost", "SL.randomForest", "SL.glmnet", "SL.nnet", "SL.ksvm", "SL.bartMachine", "SL.kernelKnn", "SL.rpartPrune", "SL.lm", "SL.mean") # Fit XGBoost, RF, Lasso, Neural Net, SVM, BART, K-nearest neighbors, Decision Tree, # OLS, and simple mean; create automatic ensemble. result = SuperLearner(Y = Boston$medv, X = Boston[, -14], SL.library = sl_lib) # Review performance of each algorithm and ensemble weights. result # Use external (aka nested) cross-validation to estimate ensemble accuracy. # This will take a while to run. result2 = CV.SuperLearner(Y = Boston$medv, X = Boston[, -14], SL.library = sl_lib) # Plot performance of individual algorithms and compare to the ensemble. plot(result2) + theme_minimal() # Hyperparameter optimization -- # Fit elastic net with 5 different alphas: 0, 0.2, 0.4, 0.6, 0.8, 1.0. # 0 corresponds to ridge and 1 to lasso. enet = create.Learner("SL.glmnet", detailed_names = T, tune = list(alpha = seq(0, 1, length.out = 5))) sl_lib2 = c("SL.mean", "SL.lm", enet$names) enet_sl = SuperLearner(Y = Boston$medv, X = Boston[, -14], SL.library = sl_lib2) # Identify the best-performing alpha value or use the automatic ensemble. enet_sl
For more detailed examples please review the vignette:
vignette(package = "SuperLearner")
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
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. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
van der Laan, M. J., & Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media.