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 adata.table::data.table()with fields "key", "label", "task_type", "feature_types", "packages", "properties", and "predict_types" as columns. Ifobjectsis set toTRUE, the constructed objects are returned in the list column namedobject.
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,stats
#> 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,weights
#> 5: featureless,importance,missings,selected_features,weights
#> 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
#> • Model: -
#> • Parameters: method=mode
#> • Packages: mlr3
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct, and Date
#> • Encapsulation: none (fallback: -)
#> • Properties: featureless, importance, missings, multiclass, selected_features,
#> twoclass, and weights
#> • Other settings: use_weights = 'use'
lrn("classif.rpart")
#>
#> ── <LearnerClassifRpart> (classif.rpart): Classification Tree ──────────────────
#> • Model: -
#> • Parameters: xval=0
#> • Packages: mlr3 and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, selected_features, twoclass,
#> and weights
#> • Other settings: use_weights = 'use'