Regression Tree Learner
Parameter xval is set to 0 in order to save some computation time.
Parameter model has been renamed to keep_model.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():
mlr_learners$get("regr.rpart")
lrn("regr.rpart")Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: rpart
| Id | Type | Default | Range | Levels |
| minsplit | integer | 20 | [1, Inf) | - |
| minbucket | integer | - | [1, Inf) | - |
| cp | numeric | 0.01 | [0, 1] | - |
| maxcompete | integer | 4 | [0, Inf) | - |
| maxsurrogate | integer | 5 | [0, Inf) | - |
| maxdepth | integer | 30 | [1, 30] | - |
| usesurrogate | integer | 2 | [0, 2] | - |
| surrogatestyle | integer | 0 | [0, 1] | - |
| xval | integer | 10 | [0, Inf) | - |
| keep_model | logical | FALSE | (-Inf, Inf) | TRUE, FALSE |
mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRpart
new()
Creates a new instance of this R6 class.
LearnerRegrRpart$new()
importance()
The importance scores are extracted from the model slot variable.importance.
LearnerRegrRpart$importance()
Named numeric().
selected_features()
Selected features are extracted from the model slot frame$var.
LearnerRegrRpart$selected_features()
character().
clone()
The objects of this class are cloneable with this method.
LearnerRegrRpart$clone(deep = FALSE)
deepWhether to make a deep clone.
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification And Regression Trees. Routledge. doi: 10.1201/9781315139470.
as.data.table(mlr_learners) for a complete table of all (also dynamically created) Learner implementations.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.