A simple mlr3misc::Dictionary storing objects of class Learner. Each learner has an associated help page, see mlr_learners_[id].

This dictionary can get populated with additional learners by add-on packages. For more classification and regression learners, load the mlr3learners package.

For a more convenient way to retrieve and construct learners, see lrn().

Format

R6::R6Class object inheriting from mlr3misc::Dictionary.

Methods

See mlr3misc::Dictionary.

S3 methods

See also

Examples

as.data.table(mlr_learners)
#> key feature_types #> 1: classif.debug logical,integer,numeric,character,factor,ordered #> 2: classif.featureless logical,integer,numeric,character,factor,ordered #> 3: classif.rpart logical,integer,numeric,factor,ordered #> 4: regr.featureless logical,integer,numeric,character,factor,ordered #> 5: regr.rpart logical,integer,numeric,factor,ordered #> packages properties #> 1: missings,multiclass,twoclass #> 2: importance,missings,multiclass,selected_features,twoclass #> 3: rpart importance,missings,multiclass,selected_features,twoclass,weights #> 4: stats importance,missings,selected_features #> 5: rpart importance,missings,selected_features,weights #> predict_types #> 1: response,prob #> 2: response,prob #> 3: response,prob #> 4: response,se #> 5: response
mlr_learners$get("classif.featureless")
#> <LearnerClassifFeatureless:classif.featureless> #> * Model: - #> * Parameters: method=mode #> * Packages: - #> * Predict Type: response #> * Feature types: logical, integer, numeric, character, factor, ordered #> * Properties: importance, missings, multiclass, selected_features, #> twoclass
lrn("classif.rpart")
#> <LearnerClassifRpart:classif.rpart> #> * Model: - #> * Parameters: xval=0 #> * Packages: rpart #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, multiclass, selected_features, #> twoclass, weights