Dictionary of Learners

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 an opinionated set of solid classification and regression learners, install and load the mlr3learners package. More learners are connected via https://github.com/mlr-org/mlr3extralearners.

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

Format

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

Methods

See mlr3misc::Dictionary.

S3 methods

• as.data.table(dict, ..., objects = FALSE)
  mlr3misc::Dictionary -> data.table::data.table()
  Returns a data.table::data.table() with fields "key", "label", "task_type", "feature_types", "packages", "properties", and "predict_types" as columns. If objects is set to TRUE, the constructed objects are returned in the list column named object.

See also

Sugar functions: lrn(), lrns()

Extension Packages: mlr3learners

Other Dictionary: mlr_measures, mlr_resamplings, mlr_task_generators, mlr_tasks

Other Learner: Learner, LearnerClassif, LearnerRegr, mlr_learners_classif.debug, mlr_learners_classif.featureless, mlr_learners_classif.rpart, mlr_learners_regr.debug, mlr_learners_regr.featureless, mlr_learners_regr.rpart

Examples

as.data.table(mlr_learners)
#> Key: <key>
#>                    key                              label task_type
#>                 <char>                             <char>    <char>
#> 1:       classif.debug   Debug Learner for Classification   classif
#> 2: classif.featureless Featureless Classification Learner   classif
#> 3:       classif.rpart                Classification Tree   classif
#> 4:          regr.debug       Debug Learner for Regression      regr
#> 5:    regr.featureless     Featureless Regression Learner      regr
#> 6:          regr.rpart                    Regression Tree      regr
#>                                           feature_types   packages
#>                                                  <list>     <list>
#> 1:     logical,integer,numeric,character,factor,ordered       mlr3
#> 2: logical,integer,numeric,character,factor,ordered,...       mlr3
#> 3:               logical,integer,numeric,factor,ordered mlr3,rpart
#> 4:     logical,integer,numeric,character,factor,ordered       mlr3
#> 5: logical,integer,numeric,character,factor,ordered,... mlr3,stats
#> 6:               logical,integer,numeric,factor,ordered mlr3,rpart
#>                                                                   properties
#>                                                                       <list>
#> 1: hotstart_forward,internal_tuning,marshal,missings,multiclass,twoclass,...
#> 2:     featureless,importance,missings,multiclass,selected_features,twoclass
#> 3:         importance,missings,multiclass,selected_features,twoclass,weights
#> 4:                                                                  missings
#> 5:                         featureless,importance,missings,selected_features
#> 6:                             importance,missings,selected_features,weights
#>            predict_types
#>                   <list>
#> 1:         response,prob
#> 2:         response,prob
#> 3:         response,prob
#> 4: response,se,quantiles
#> 5: response,se,quantiles
#> 6:              response
mlr_learners$get("classif.featureless")
#> <LearnerClassifFeatureless:classif.featureless>: Featureless Classification Learner
#> * Model: -
#> * Parameters: method=mode
#> * Packages: mlr3
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, character, factor, ordered,
#>   POSIXct
#> * Properties: featureless, importance, missings, multiclass,
#>   selected_features, twoclass
lrn("classif.rpart")
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: -
#> * Parameters: xval=0
#> * Packages: mlr3, rpart
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights