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Functions to retrieve objects, set hyperparameters and assign to fields in one go. Relies on mlr3misc::dictionary_sugar_get() to extract objects from the respective mlr3misc::Dictionary:

Usage

tsk(.key, ...)

tsks(.keys, ...)

tgen(.key, ...)

tgens(.keys, ...)

lrn(.key, ...)

lrns(.keys, ...)

rsmp(.key, ...)

rsmps(.keys, ...)

msr(.key, ...)

msrs(.keys, ...)

Arguments

.key

(character(1))
Key passed to the respective dictionary to retrieve the object.

...

(named list())
Named arguments passed to the constructor, to be set as parameters in the paradox::ParamSet, or to be set as public field. See mlr3misc::dictionary_sugar_get() for more details.

.keys

(character())
Keys passed to the respective dictionary to retrieve multiple objects.

Value

R6::R6Class object of the respective type, or a list of R6::R6Class objects for the plural versions.

Examples

# penguins task with new id
tsk("penguins", id = "penguins2")
#> <TaskClassif:penguins2> (344 x 8): Palmer Penguins
#> * Target: species
#> * Properties: multiclass
#> * Features (7):
#>   - int (3): body_mass, flipper_length, year
#>   - dbl (2): bill_depth, bill_length
#>   - fct (2): island, sex

# classification tree with different hyperparameters
# and predict type set to predict probabilities
lrn("classif.rpart", cp = 0.1, predict_type = "prob")
#> <LearnerClassifRpart:classif.rpart>: Classification Tree
#> * Model: -
#> * Parameters: xval=0, cp=0.1
#> * Packages: mlr3, rpart
#> * Predict Types:  response, [prob]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, multiclass, selected_features,
#>   twoclass, weights

# multiple learners with predict type 'prob'
lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
#> [[1]]
#> <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
#> 
#> [[2]]
#> <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
#>