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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:
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.
tsk(.key, ...)tsks(.keys, ...)
tgen(.key, ...)
tgens(.keys, ...)
lrn(.key, ...)
lrns(.keys, ...)
rsmp(.key, ...)
rsmps(.keys, ...)
msr(.key, ...)
msrs(.keys, ...)
(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.
(character()
)
Keys passed to the respective dictionary to retrieve multiple objects.
R6::R6Class object of the respective type, or a list of R6::R6Class objects for the plural versions.
# NOT RUN {
# penguins task with new id
tsk("penguins", id = "penguins2")
# classification tree with different hyperparameters
# and predict type set to predict probabilities
lrn("classif.rpart", cp = 0.1, predict_type = "prob")
# multiple learners with predict type 'prob'
lrns(c("classif.featureless", "classif.rpart"), predict_type = "prob")
# }
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