R: Rpart Survival Trees Survival Learner

mlr_learners_surv.rpart {mlr3proba} | R Documentation

## Rpart Survival Trees Survival Learner

### Description

Calls `rpart::rpart()`.

- crank is predicted using `rpart::predict.rpart()`

Parameter `xval` is set to 0 in order to save some computation time.

### Dictionary

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

    LearnerSurvRpart$new()
    mlr_learners$get("surv.rpart")
    lrn("surv.rpart")

### Meta Information

- Type: "surv"

- Predict Types: `crank, distr`

- Feature Types: `logical, integer, numeric, character, factor, ordered`

- Properties: `importance, missings, selected_features, weights`

- Packages: mlr3 mlr3proba rpart distr6 survival

### Super classes

`mlr3::Learner` -> `mlr3proba::LearnerSurv` -> `LearnerSurvRpart`

### Methods

#### Public methods

- `LearnerSurvRpart$new()`

- `LearnerSurvRpart$importance()`

- `LearnerSurvRpart$selected_features()`

- `LearnerSurvRpart$clone()`

Inherited methods

- `mlr3::Learner$base_learner()`

- `mlr3::Learner$format()`

- `mlr3::Learner$help()`

- `mlr3::Learner$predict()`

- `mlr3::Learner$predict_newdata()`

- `mlr3::Learner$print()`

- `mlr3::Learner$reset()`

- `mlr3::Learner$train()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    LearnerSurvRpart$new()

#### Method `importance()`

The importance scores are extracted from the model slot `variable.importance`.

##### Usage

    LearnerSurvRpart$importance()

##### Returns

Named `numeric()`.

#### Method `selected_features()`

Selected features are extracted from the model slot `frame$var`.

##### Usage

    LearnerSurvRpart$selected_features()

##### Returns

`character()`.

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    LearnerSurvRpart$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### References

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984). Classification And Regression Trees. Routledge. doi: 10.1201/9781315139470.

### See Also

Other survival learners: `mlr_learners_surv.coxph`, `mlr_learners_surv.kaplan`

[Package mlr3proba version 0.4.3 Index]