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 package as follows:
# latest GitHub release
remotes::install_github("mlr-org/mlr3extralearners@*release")
# development version
remotes::install_github("mlr-org/mlr3extralearners")
Alternatively, you can add the following to your .Rprofile, which allows
you to install mlr3extralearners
via install.packages()
. Note that
this will install the development version.
# .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, weights
An in-depth tutorial on how to add learners can be found in the package website.