Description Custom mlr3 defaults Dictionary Super classes Methods References See Also Examples
Gradient boosted decision trees regression learner.
Uses catboost::catboost.train()
and catboost::catboost.predict()
from
package catboost.
By default, the RMSE is optimized as the loss function.
logging_level
:
Actual default: "Verbose"
Adjusted default: "Silent"
Reason for change: consistent with other mlr3 learners
thread_count
:
Actual default: -1
Adjusted default: 1
Reason for change: consistent with other mlr3 learners
allow_writing_files
:
Actual default: TRUE
Adjusted default: FALSE
Reason for change: consistent with other mlr3 learners
save_snapshot
:
Actual default: TRUE
Adjusted default: FALSE
Reason for change: consistent with other mlr3 learners
This Learner can be instantiated via the dictionary
mlr_learners or with the associated sugar function lrn()
:
1 2 | mlr_learners$get("regr.catboost")
lrn("regr.catboost")
|
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrCatboost
new()
Create a LearnerRegrCatboost
object.
LearnerRegrCatboost$new()
importance()
The importance scores are calculated using
catboost.get_feature_importance
,
setting type = "FeatureImportance"
, returned for 'all'.
LearnerRegrCatboost$importance()
Named numeric()
.
clone()
The objects of this class are cloneable with this method.
LearnerRegrCatboost$clone(deep = FALSE)
deep
Whether to make a deep clone.
mlr3learners.catboostdorogush2019 mlr3learners.catboostprokhorenkova2017
Dictionary of Learners: mlr3::mlr_learners
1 2 3 4 5 6 7 | if (requireNamespace("catboost")) {
learner = mlr3::lrn("regr.catboost")
print(learner)
# available parameters:
learner$param_set$ids()
}
|
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