Package website: release | dev
Extra Learners for mlr3.
mlr3extralearners contains all learners from mlr3 that are not in
mlr3learners or the core packages. An overview of all learners within
the mlr3verse can be found here.
mlr3extralearners lives on GitHub and will not be on CRAN.
You can install the latest release using the code below
remotes::install_github("mlr-org/mlr3extralearners@*release")Alternatively, you can add the following to your .Rprofile, which allows
you to install mlr3extralearners via install.packages().
# .Rprofile
options(repos = c(
mlrorg = "https://mlr-org.r-universe.dev",
CRAN = "https://cloud.r-project.org/"
))The package includes functionality for detecting if you have the
required packages installed to use a learner, and ships with the
function install_learner which can install all required learner
dependencies.
lrn("regr.gbm")
#> Warning: Package 'gbm' required but not installed for Learner 'regr.gbm'
#> <LearnerRegrGBM:regr.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weights
install_learners("regr.gbm")
lrn("regr.gbm")
#> <LearnerRegrGBM:regr.gbm>: Gradient Boosting
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: mlr3, mlr3extralearners, gbm
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weightsNew learners - either for personal use or to extend mlr3extralearners -
can be created with the create_learner function. An in-depth tutorial
on how to do this can be found in the mlr3
book.