mlr_learners_regr.catboost: Gradient Boosted Decision Trees Regression Learner

Description Custom mlr3 defaults Dictionary Super classes Methods References See Also Examples

Description

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.

Custom mlr3 defaults

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

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mlr_learners$get("regr.catboost")
lrn("regr.catboost")

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCatboost

Methods

Public methods

Inherited methods

Method new()

Create a LearnerRegrCatboost object.

Usage
LearnerRegrCatboost$new()

Method importance()

The importance scores are calculated using catboost.get_feature_importance, setting type = "FeatureImportance", returned for 'all'.

Usage
LearnerRegrCatboost$importance()
Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRegrCatboost$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

mlr3learners.catboostdorogush2019 mlr3learners.catboostprokhorenkova2017

See Also

Dictionary of Learners: mlr3::mlr_learners

Examples

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if (requireNamespace("catboost")) {
  learner = mlr3::lrn("regr.catboost")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr3learners/mlr3learners.catboost documentation built on July 27, 2020, 7:30 a.m.