LearnerRpart: LearnerRpart R6 class

In mlexperiments: Machine Learning Experiments

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 mean misclassification error rate (measures::MMCE()) is used as the optimization metric for classification tasks and the mean squared error (measures::MSE()) is used for regression tasks.

Super class

mlexperiments::MLLearnerBase -> LearnerRpart

Methods

Public methods

• LearnerRpart$new()

• LearnerRpart$clone()

Inherited methods

• mlexperiments::MLLearnerBase$bayesian_scoring_function()
• mlexperiments::MLLearnerBase$cross_validation()
• mlexperiments::MLLearnerBase$fit()
• mlexperiments::MLLearnerBase$predict()

---

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 mean misclassification error (measures::MMCE()) is used as the optimization metric for classification tasks and the mean squared error (measures::MSE()) is used for regression tasks.

Examples

if (requireNamespace("rpart", quietly = TRUE)) {
  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(), measures::MMCE(), [measures::MSE)], [rpart::rpart.control()]

[measures::MSE)]: R:measures::MSE) [rpart::rpart.control()]: R:rpart::rpart.control()

rpart::rpart(), measures::MMCE(), measures::MSE()

Examples

if (requireNamespace("rpart", quietly = TRUE)) {
  LearnerRpart$new()
}


## ------------------------------------------------
## Method `LearnerRpart$new`
## ------------------------------------------------

if (requireNamespace("rpart", quietly = TRUE)) {
  LearnerRpart$new()
}

mlexperiments documentation built on Jan. 16, 2026, 5:17 p.m.