listLearners | R Documentation |
Returns learning algorithms which have specific characteristics, e.g. whether they support missing values, case weights, etc.
Note that the packages of all learners are loaded during the search if you create them. This can be a lot. If you do not create them we only inspect properties of the S3 classes. This will be a lot faster.
Note that for general cost-sensitive learning, mlr currently supports mainly “wrapper” approaches like CostSensWeightedPairsWrapper, which are not listed, as they are not basic R learning algorithms. The same applies for many multilabel methods, see, e.g., makeMultilabelBinaryRelevanceWrapper.
listLearners(
obj = NA_character_,
properties = character(0L),
quiet = TRUE,
warn.missing.packages = TRUE,
check.packages = FALSE,
create = FALSE
)
## Default S3 method:
listLearners(
obj = NA_character_,
properties = character(0L),
quiet = TRUE,
warn.missing.packages = TRUE,
check.packages = FALSE,
create = FALSE
)
## S3 method for class 'character'
listLearners(
obj = NA_character_,
properties = character(0L),
quiet = TRUE,
warn.missing.packages = TRUE,
check.packages = FALSE,
create = FALSE
)
## S3 method for class 'Task'
listLearners(
obj = NA_character_,
properties = character(0L),
quiet = TRUE,
warn.missing.packages = TRUE,
check.packages = TRUE,
create = FALSE
)
obj |
( |
properties |
(character) |
quiet |
( |
warn.missing.packages |
( |
check.packages |
( |
create |
( |
([data.frame|
list' of Learner).
Either a descriptive data.frame that allows access to all properties of the learners
or a list of created learner objects (named by ids of listed learners).
## Not run:
listLearners("classif", properties = c("multiclass", "prob"))
data = iris
task = makeClassifTask(data = data, target = "Species")
listLearners(task)
## End(Not run)
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