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Extra Learners for mlr3.
What is mlr3extralearners?
mlr3extralearners contains all learners from mlr3 that are not in mlr3learners or the core packages. mlr3extralearners contains helper functions to find where all the learners, across the mlr3verse, live and to install required packages to run these learners. See the interactive learner list for the full list of learners in the mlr3verse and the learner status page for a live build status.
list_mlr3learners(select = c("id", "mlr3_package", "required_packages"))
#> id mlr3_package required_packages
#> 1: classif.AdaBoostM1 mlr3extralearners RWeka
#> 2: classif.bart mlr3extralearners dbarts
#> 3: classif.C50 mlr3extralearners C50
#> 4: classif.catboost mlr3extralearners catboost
#> 5: classif.cforest mlr3extralearners partykit,sandwich,coin
#> ---
#> 128: surv.ranger mlr3learners ranger
#> 129: surv.rfsrc mlr3extralearners randomForestSRC,pracma
#> 130: surv.rpart mlr3proba rpart,distr6,survival
#> 131: surv.svm mlr3extralearners survivalsvm
#> 132: surv.xgboost mlr3learners xgboostmlr3extralearners lives on GitHub and will not be on CRAN. Install with:
remotes::install_github("mlr-org/mlr3extralearners")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")
#> Error: Required packages not installed, please run `install_learners("regr.gbm")`.
install_learners("regr.gbm")
lrn("regr.gbm")
#> <LearnerRegrGBM:regr.gbm>
#> * Model: -
#> * Parameters: keep.data=FALSE, n.cores=1
#> * Packages: gbm
#> * Predict Type: response
#> * Feature types: integer, numeric, factor, ordered
#> * Properties: importance, missings, weightsExtending mlr3extralearners
New learners can be created with the create_learner function. This assumes you have a local copy of mlr3extralearners. This function will automatically create the learner, learner tests, parameter tests, YAML files for CI if required, and update the DESCRIPTION if required. Once all tests are passing locally, open a pull request with the “New Learner” template.
create_learner(classname = "Locfit",
algorithm = "localised fit",
type = "dens",
key = "locfit",
package = "locfit",
caller = "density.lf",
feature_types = c("integer", "numeric"),
predict_types = c("pdf", "cdf"),
properties = NULL,
importance = FALSE,
oob_error = FALSE,
references = FALSE,
gh_name = "RaphaelS1")