damirpolat / mlr3extralearners

Extra learners for use in mlr3.

mlr3extralearners

Package website: release | dev

Extra Learners for mlr3.

What is mlr3extralearners?

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/"
))

Installing and Loading Learners

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

Extending mlr3extralearners

An in-depth tutorial on how to add learners can be found in the package website.