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:
tgen()
for a TaskGenerator from mlr_task_generators.tgens()
for a list of TaskGenerators from mlr_task_generators.lrn()
for a Learner from mlr_learners.lrns()
for a list of Learners from mlr_learners.rsmp()
for a Resampling from mlr_resamplings.rsmps()
for a list of Resamplings from mlr_resamplings.msr()
for a Measure from mlr_measures.msrs()
for a list of Measures from mlr_measures.
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. Seemlr3misc::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")
#> $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
#>
#> $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
#>