An modern implementation of the Super Learner prediction algorithm using the mlr3 framework, and an adherence to the recommendations of Phillips, van der Laan, Lee, and Gruber (2023)
You can install the development version of mlr3superlearner from GitHub with:
# install.packages("devtools")
devtools::install_github("nt-williams/mlr3superlearner")library(mlr3superlearner)
#> Loading required package: mlr3learners
#> Loading required package: mlr3
library(mlr3extralearners)
# No hyperparameters
mlr3superlearner(mtcars, "mpg", c("mean", "glm", "svm", "ranger"), "continuous")
#> ℹ n effective = 32. Setting cross-validation folds as 20
#> ══ `mlr3superlearner()` ════════════════════════════════════════════════════════
#> Risk Coefficients
#> regr.featureless 37.82487 0
#> regr.lm 12.21920 0
#> regr.ranger 5.70615 1
#> regr.svm 11.38053 0
# With hyperparameters
fit <- mlr3superlearner(mtcars, "mpg",
list("mean", "glm", "xgboost", "svm", "earth",
list("nnet", trace = FALSE),
list("ranger", num.trees = 500, id = "ranger1"),
list("ranger", num.trees = 1000, id = "ranger2")),
"continuous")
#> ℹ n effective = 32. Setting cross-validation folds as 20
fit
#> ══ `mlr3superlearner()` ════════════════════════════════════════════════════════
#> Risk Coefficients
#> regr.earth 7.781849 0
#> regr.glm 12.166336 0
#> regr.mean 37.891555 0
#> regr.nnet_and_trace_FALSE 36.086681 0
#> regr.ranger1 5.953228 0
#> regr.ranger2 5.724181 1
#> regr.svm 11.223307 0
#> regr.xgboost 225.854354 0
head(data.frame(pred = predict(fit, mtcars), truth = mtcars$mpg))
#> pred truth
#> 1 20.74050 21.0
#> 2 20.70535 21.0
#> 3 24.25158 22.8
#> 4 20.21326 21.4
#> 5 17.67443 18.7
#> 6 18.98955 18.1knitr::kable(available_learners("binomial"))| learner | mlr3_learner | mlr3_package | learner_package |
|---|---|---|---|
| mean | classif.featureless | mlr3 | stats |
| glm | classif.log_reg | mlr3learners | stats |
| glmnet | classif.glmnet | mlr3learners | glmnet |
| cv_glmnet | classif.cv_glmnet | mlr3learners | glmnet |
| knn | classif.kknn | mlr3learners | kknn |
| nnet | classif.nnet | mlr3learners | nnet |
| lda | classif.lda | mlr3learners | MASS |
| naivebayes | classif.naive_bayes | mlr3learners | e1071 |
| qda | classif.qda | mlr3learners | MASS |
| ranger | classif.ranger | mlr3learners | ranger |
| svm | classif.svm | mlr3learners | e1071 |
| xgboost | classif.xgboost | mlr3learners | xgboost |
| earth | classif.earth | mlr3extralearners | earth |
| lightgbm | classif.lightgbm | mlr3extralearners | lightgbm |
| randomforest | classif.randomForest | mlr3extralearners | randomForest |
| bart | classif.bart | mlr3extralearners | dbarts |
| c50 | classif.C50 | mlr3extralearners | C50 |
| gam | classif.gam | mlr3extralearners | mgcv |
| gaussianprocess | classif.gausspr | mlr3extralearners | kernlab |
| glmboost | classif.glmboost | mlr3extralearners | mboost |
| nloptr | classif.avg | mlr3pipelines | nloptr |
| rpart | classif.rpart | mlr3 | rpart |
knitr::kable(available_learners("continuous"))| learner | mlr3_learner | mlr3_package | learner_package |
|---|---|---|---|
| mean | regr.featureless | mlr3 | stats |
| glm | regr.lm | mlr3learners | stats |
| glmnet | regr.glmnet | mlr3learners | glmnet |
| cv_glmnet | regr.cv_glmnet | mlr3learners | glmnet |
| knn | regr.kknn | mlr3learners | kknn |
| nnet | regr.nnet | mlr3learners | nnet |
| ranger | regr.ranger | mlr3learners | ranger |
| svm | regr.svm | mlr3learners | e1071 |
| xgboost | regr.xgboost | mlr3learners | xgboost |
| earth | regr.earth | mlr3extralearners | earth |
| lightgbm | regr.lightgbm | mlr3extralearners | lightgbm |
| randomforest | regr.randomForest | mlr3extralearners | randomForest |
| bart | regr.bart | mlr3extralearners | dbarts |
| gam | regr.gam | mlr3extralearners | mgcv |
| gaussianprocess | regr.gausspr | mlr3extralearners | kernlab |
| glmboost | regr.glmboost | mlr3extralearners | mboost |
| rpart | regr.rpart | mlr3 | rpart |