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. 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", "required_packages"))
#> This will take a few seconds.
#> id required_packages
#> <char> <list>
#> 1: classif.AdaBoostM1 mlr3,mlr3extralearners,RWeka
#> 2: classif.bart mlr3,mlr3extralearners,dbarts
#> 3: classif.C50 mlr3,mlr3extralearners,C50
#> 4: classif.catboost mlr3,mlr3extralearners,catboost
#> 5: classif.cforest mlr3,mlr3extralearners,partykit,sandwich,coin
#> ---
#> 133: surv.ranger mlr3,mlr3proba,mlr3extralearners,ranger
#> 134: surv.rfsrc mlr3,mlr3proba,mlr3extralearners,randomForestSRC,pracma
#> 135: surv.rpart mlr3,mlr3proba,rpart,distr6,survival
#> 136: surv.svm mlr3,mlr3proba,mlr3extralearners,survivalsvm
#> 137: surv.xgboost mlr3,mlr3proba,mlr3extralearners,xgboostmlr3extralearners lives on GitHub and will not be on CRAN. You can download the latest release from here and install it locally with
devtools::install_local("path/to/mlr3extralearners")If you want to download the development version, run
devtools::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")
#> 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, weightsinstall_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, weightsExtending 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.