LearnerRpart | R Documentation |
This learner is a wrapper around rpart::rpart()
in order to fit recursive
partitioning and regression trees.
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).
$bayesian_scoring_function
To perform a Bayesian hyperparameter
optimization.
Parameters that are specified with parameter_grid
and / or learner_args
are forwarded to rpart
's argument control
(see
rpart::rpart.control()
for further details).
For the two hyperparameter optimization strategies ("grid" and "bayesian"),
the parameter metric_optimization_higher_better
of the learner is
set to FALSE
by default as the classification error rate
(mlr3measures::ce()
) is used as the optimization metric for
classification tasks and the mean squared error (mlr3measures::mse()
) is
used for regression tasks.
mlexperiments::MLLearnerBase
-> LearnerRpart
new()
Create a new LearnerRpart
object.
LearnerRpart$new()
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.
LearnerRpart$new()
clone()
The objects of this class are cloneable with this method.
LearnerRpart$clone(deep = FALSE)
deep
Whether to make a deep clone.
rpart::rpart()
, mlr3measures::ce()
, mlr3measures::mse()
,
rpart::rpart.control()
rpart::rpart()
, mlr3measures::ce()
, mlr3measures::mse()
LearnerRpart$new()
## ------------------------------------------------
## Method `LearnerRpart$new`
## ------------------------------------------------
LearnerRpart$new()
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