Super Learner Fitting and Prediction.

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"))

knitr::kable(available_learners("continuous"))