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Gradient boosting with regression trees for survival analysis. Calls mboost::blackboost() from 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")

Meta Information

Parameters

IdTypeDefaultLevelsRange
familycharactercoxphcoxph, weibull, loglog, lognormal, gehan, cindex, custom-
custom.familyuntyped--
nuirangeuntypedc , 0 , 100-
offsetuntyped--
centerlogicalTRUETRUE, FALSE-
mstopinteger100\([0, \infty)\)
nunumeric0.1\([0, 1]\)
riskcharacter-inbag, oobag, none-
stopinternlogicalFALSETRUE, FALSE-
tracelogicalFALSETRUE, FALSE-
oobweightsuntyped--
teststatcharacterquadraticquadratic, maximum-
splitstatcharacterquadraticquadratic, maximum-
splittestlogicalFALSETRUE, FALSE-
testtypecharacterBonferroniBonferroni, MonteCarlo, Univariate, Teststatistic-
maxptsinteger25000\([1, \infty)\)
absepsnumeric0.001\((-\infty, \infty)\)
relepsnumeric0\((-\infty, \infty)\)
nmaxuntyped--
alphanumeric0.05\([0, 1]\)
mincriterionnumeric0.95\([0, 1]\)
logmincriterionnumeric-0.05129329\((-\infty, 0]\)
minsplitinteger20\([0, \infty)\)
minbucketinteger7\([0, \infty)\)
minprobnumeric0.01\([0, 1]\)
stumplogicalFALSETRUE, FALSE-
lookaheadlogicalFALSETRUE, FALSE-
MIAlogicalFALSETRUE, FALSE-
nresampleinteger9999\([1, \infty)\)
tolnumeric1.490116e-08\([0, \infty)\)
maxsurrogateinteger0\([0, \infty)\)
mtryinteger-\([0, \infty)\)
maxdepthinteger-\([0, \infty)\)
multiwaylogicalFALSETRUE, FALSE-
splittryinteger2\([1, \infty)\)
intersplitlogicalFALSETRUE, FALSE-
majoritylogicalFALSETRUE, FALSE-
caseweightslogicalTRUETRUE, FALSE-
sigmanumeric0.1\([0, 1]\)
ipcwuntyped1-
na.actionuntyped:: , stats , na.omit-

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324--339.

See also

Author

RaphaelS1

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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

learner = mlr3::lrn("surv.blackboost")
print(learner)
#> <LearnerSurvBlackBoost:surv.blackboost>: Gradient Boosting
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, 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"