Skip to content

SuperLearner R package: prediction model ensembling method

Notifications You must be signed in to change notification settings

ck37/SuperLearner

This branch is 90 commits behind ecpolley/SuperLearner:master.

Folders and files

NameName
Last commit message
Last commit date

Latest commit

afc78cf · Aug 6, 2019
Jul 17, 2019
Jul 25, 2019
Jul 17, 2019
Jul 10, 2018
Aug 6, 2019
Jun 21, 2017
Jun 21, 2017
Aug 28, 2018
Jul 10, 2018
Jun 21, 2017
Mar 9, 2018
Jun 21, 2017
Jun 21, 2017
Aug 10, 2016

Repository files navigation

SuperLearner: Prediction model ensembling method

Join the chat at https://gitter.im/SuperLearner CRAN_Status_Badge Downloads Build Status: travis-ci Build Status: appveyor codecov

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("devtools")
devtools::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. http://www.targetedlearningbook.com

Releases

No releases published

Packages

No packages published

Languages

  • R 99.9%
  • Makefile 0.1%