LearnerRpart: LearnerRpart R6 class

LearnerRpartR Documentation

LearnerRpart R6 class

Description

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees.

Details

Optimization metric:

  • classification (method = "class"): classification error rate

  • regression (method = "anova"): mean squared error

Can be used with

  • MLTuneParameters

  • MLCrossValidation

  • MLNestedCV

Implemented methods:

  • ⁠$fit⁠ To fit the model.

  • ⁠$predict⁠ To predict new data with the model.

  • ⁠$cross_validation⁠ To perform a grid search (hyperparameter optimization).

  • ⁠$bayesian_scoring_function⁠ To perform a Bayesian hyperparameter optimization.

Parameters that are specified with parameter_grid and / or learner_args are forwarded to rpart's argument control (see rpart::rpart.control() for further details).

For the two hyperparameter optimization strategies ("grid" and "bayesian"), the parameter metric_optimization_higher_better of the learner is set to FALSE by default as the classification error rate (mlr3measures::ce()) is used as the optimization metric for classification tasks and the mean squared error (mlr3measures::mse()) is used for regression tasks.

Super class

mlexperiments::MLLearnerBase -> LearnerRpart

Methods

Public methods

Inherited methods

Method new()

Create a new LearnerRpart object.

Usage
LearnerRpart$new()
Details

This learner is a wrapper around rpart::rpart() in order to fit recursive partitioning and regression trees. The following experiments are implemented:

  • MLTuneParameters

  • MLCrossValidation

  • MLNestedCV

For the two hyperparameter optimization strategies ("grid" and "bayesian"), the parameter metric_optimization_higher_better of the learner is set to FALSE by default as the classification error rate (mlr3measures::ce()) is used as the optimization metric for classification tasks and the mean squared error (mlr3measures::mse()) is used for regression tasks.

Examples
LearnerRpart$new()


Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRpart$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

rpart::rpart(), mlr3measures::ce(), mlr3measures::mse(), rpart::rpart.control()

rpart::rpart(), mlr3measures::ce(), mlr3measures::mse()

Examples

LearnerRpart$new()


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## Method `LearnerRpart$new`
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LearnerRpart$new()


mlexperiments documentation built on April 12, 2025, 1:40 a.m.