This Learner specializes Learner for regression problems:
task_type is set to
Possible values for
Predefined learners can be found in the dictionary mlr_learners. Essential regression learners can be found in this dictionary after loading mlr3learners. Additional learners are implement in the Github package https://github.com/mlr-org/mlr3extralearners.
Creates a new instance of this R6 class.
Identifier for the new instance.
Set of hyperparameters.
"missings": The learner can handle missing values in the data.
"weights": The learner supports observation weights.
"importance": The learner supports extraction of importance scores, i.e. comes with an
$importance() extractor function (see section on optional extractors in Learner).
"selected_features": The learner supports extraction of the set of selected features, i.e. comes with a
$selected_features() extractor function (see section on optional extractors in Learner).
"oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a
oob_error() extractor function (see section on optional extractors in Learner).
Set of supported data formats which can be processed during
String in the format
[pkg]::[topic] pointing to a manual page for this object.
The referenced help package can be opened via method
The objects of this class are cloneable with this method.
LearnerRegr$clone(deep = FALSE)
Whether to make a deep clone.
# get all regression learners from mlr_learners: lrns = mlr_learners$mget(mlr_learners$keys("^regr")) names(lrns)#>  "regr.featureless" "regr.rpart"# get a specific learner from mlr_learners: mlr_learners$get("regr.rpart")#> <LearnerRegrRpart:regr.rpart> #> * Model: - #> * Parameters: xval=0 #> * Packages: rpart #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, selected_features, weightslrn("classif.featureless")#> <LearnerClassifFeatureless:classif.featureless> #> * Model: - #> * Parameters: method=mode #> * Packages: - #> * Predict Type: response #> * Feature types: logical, integer, numeric, character, factor, ordered, #> POSIXct #> * Properties: featureless, importance, missings, multiclass, #> selected_features, twoclass