Description Format Construction Fields Methods See Also Examples
This Learner specializes Learner for classification problems.
Many predefined learners can be found in the mlr3misc::Dictionary mlr_learners after loading the mlr3learners package.
R6::R6Class object inheriting from Learner.
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For a description of the arguments, see Learner.
task_type
is set to "classif"
.
Possible values for predict_types
are passed to and converted by PredictionClassif:
"response"
: Predicts a class label for each observation in the test set.
"prob"
: Predicts the posterior probability for each class for each observation in the test set.
Additional learner properties include:
"twoclass"
: The learner works on binary classification problems.
"multiclass"
: The learner works on multiclass classification problems.
See Learner.
See Learner.
Example classification learners: classif.rpart
Other Learner: LearnerRegr
,
Learner
, mlr_learners
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # get all classification learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^classif"))
names(lrns)
# get a specific learner from mlr_learners:
lrn = lrn("classif.rpart")
print(lrn)
# train the learner:
task = tsk("iris")
lrn$train(task, 1:120)
# predict on new observations:
lrn$predict(task, 121:150)$confusion
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