SuperLearner: Prediction model ensembling method

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

Features

• 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:

install.packages("SuperLearner")

Examples

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")

References

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.