mlr3superlearner

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)

Installation

You can install the development version of mlr3superlearner from GitHub with:

# install.packages("devtools")
devtools::install_github("nt-williams/mlr3superlearner")

Example

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  18.1

Available learners

knitr::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