Gradient Boosting with Regression Trees Survival Learner

Calls mboost::blackboost from package mboost.

Details

distr prediction made by mboost::survFit().

Dictionary

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

mlr_learners$get("surv.blackboost")
lrn("surv.blackboost")

Traits

• Packages: mboost, pracma

• Predict Types: distr, crank, lp

• Feature Types: integer, numeric, factor

• Properties: weights

References

Bühlmann P, Yu B (2003). “Boosting With the L2 Loss.” Journal of the American Statistical Association, 98(462), 324–339. doi: 10.1198/016214503000125

See also

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlackBoost

Methods

Public methods

• LearnerSurvBlackBoost$new()

• LearnerSurvBlackBoost$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvBlackBoost$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvBlackBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("surv.blackboost"))
print(learner)
#> <LearnerSurvBlackBoost:surv.blackboost>
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mboost, pracma
#> * Predict Type: distr
#> * Feature types: integer, numeric, factor
#> * Properties: weights

# available parameters:
learner$param_set$ids()
#>  [1] "family"          "custom.family"   "nuirange"        "offset"         
#>  [5] "center"          "mstop"           "nu"              "risk"           
#>  [9] "stopintern"      "trace"           "oobweights"      "teststat"       
#> [13] "splitstat"       "splittest"       "testtype"        "maxpts"         
#> [17] "abseps"          "releps"          "nmax"            "alpha"          
#> [21] "mincriterion"    "logmincriterion" "minsplit"        "minbucket"      
#> [25] "minprob"         "stump"           "lookahead"       "MIA"            
#> [29] "nresample"       "tol"             "maxsurrogate"    "mtry"           
#> [33] "maxdepth"        "multiway"        "splittry"        "intersplit"     
#> [37] "majority"        "caseweights"     "sigma"           "ipcw"           
#> [41] "na.action"