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Survival Learner

Source: R/LearnerSurv.R
LearnerSurv.Rd

This Learner specializes Learner for survival problems:

  • task_type is set to "surv"

  • Creates Predictions of class PredictionSurv.

  • Possible values for predict_types are:

    • "distr": Predicts a probability distribution for each observation in the test set, uses distr6.

    • "lp": Predicts a linear predictor for each observation in the test set.

    • "crank": Predicts a continuous ranking for each observation in the test set.

    • "response": Predicts a survival time for each observation in the test set.

See also

Other Learner: LearnerDens

Super class

mlr3::Learner -> LearnerSurv

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurv$new(
  id,
  param_set = ps(),
  predict_types = "distr",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

id

(character(1))
Identifier for the new instance.

param_set

(paradox::ParamSet)
Set of hyperparameters.

predict_types

(character())
Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

feature_types

(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

properties

(character())
Set of properties of the Learner. Must be a subset of mlr_reflections$learner_properties. The following properties are currently standardized and understood by learners in mlr3:

  • "missings": The learner can handle missing values in the data.

  • "weights": The learner supports observation weights.

  • "importance": The learner supports extraction of importance scores, i.e. comes with an $importance() extractor function (see section on optional extractors in Learner).

  • "selected_features": The learner supports extraction of the set of selected features, i.e. comes with a $selected_features() extractor function (see section on optional extractors in Learner).

  • "oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a oob_error() extractor function (see section on optional extractors in Learner).

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

(character(1))
Label for the new instance.

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
# get all survival learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^surv"))
names(lrns)
#> [1] "surv.coxph"  "surv.kaplan" "surv.rpart" 

# get a specific learner from mlr_learners:
mlr_learners$get("surv.coxph")
#> <LearnerSurvCoxPH:surv.coxph>: Cox Proportional Hazards
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, survival, distr6
#> * Predict Types:  [crank], distr, lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights
lrn("surv.coxph")
#> <LearnerSurvCoxPH:surv.coxph>: Cox Proportional Hazards
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, survival, distr6
#> * Predict Types:  [crank], distr, lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: weights