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 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()
.
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
New 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.