Description Custom mlr3 defaults Dictionary Super classes Methods See Also Examples
Classification gradient boosting machine models.
Calls gbm::gbm()
from package gbm.
keep_data
:
Actual default: TRUE
Adjusted default: FALSE
Reason for change: keep_data = FALSE
saves memory during model fitting.
n.cores
:
Actual default: NULL
Adjusted default: 1
Reason for change: Suppressing the automatic internal parallelization if
cv.folds
> 0.
This Learner can be instantiated via the
dictionary mlr_learners or with the associated
sugar function lrn()
:
1 2 | mlr_learners$get("classif.gbm")
lrn("classif.gbm")
|
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifGBM
new()
Creates a new instance of this R6 class.
LearnerClassifGBM$new()
importance()
The importance scores are extracted by gbm::relative.influence()
from
the model.
LearnerClassifGBM$importance()
Named numeric()
.
clone()
The objects of this class are cloneable with this method.
LearnerClassifGBM$clone(deep = FALSE)
deep
Whether to make a deep clone.
Dictionary of Learners: mlr3::mlr_learners
1 2 3 4 5 6 7 | if (requireNamespace("gbm")) {
learner = mlr3::lrn("classif.gbm")
print(learner)
# available parameters:
learner$param_set$ids()
}
|
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