Gradient Boosting with Regression Trees Survival Learner
mlr_learners_surv.blackboost.Rd
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()
:
$get("surv.blackboost")
mlr_learnerslrn("surv.blackboost")
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
Parameters
Id | Type | Default | Levels | Range |
family | character | coxph | coxph, weibull, loglog, lognormal, gehan, cindex, custom | - |
custom.family | untyped | - | - | |
nuirange | untyped | c , 0 , 100 | - | |
offset | untyped | - | - | |
center | logical | TRUE | TRUE, FALSE | - |
mstop | integer | 100 | \([0, \infty)\) | |
nu | numeric | 0.1 | \([0, 1]\) | |
risk | character | - | inbag, oobag, none | - |
stopintern | logical | FALSE | TRUE, FALSE | - |
trace | logical | FALSE | TRUE, FALSE | - |
oobweights | untyped | - | - | |
teststat | character | quadratic | quadratic, maximum | - |
splitstat | character | quadratic | quadratic, maximum | - |
splittest | logical | FALSE | TRUE, FALSE | - |
testtype | character | Bonferroni | Bonferroni, MonteCarlo, Univariate, Teststatistic | - |
maxpts | integer | 25000 | \([1, \infty)\) | |
abseps | numeric | 0.001 | \((-\infty, \infty)\) | |
releps | numeric | 0 | \((-\infty, \infty)\) | |
nmax | untyped | - | - | |
alpha | numeric | 0.05 | \([0, 1]\) | |
mincriterion | numeric | 0.95 | \([0, 1]\) | |
logmincriterion | numeric | -0.05129329 | \((-\infty, 0]\) | |
minsplit | integer | 20 | \([0, \infty)\) | |
minbucket | integer | 7 | \([0, \infty)\) | |
minprob | numeric | 0.01 | \([0, 1]\) | |
stump | logical | FALSE | TRUE, FALSE | - |
lookahead | logical | FALSE | TRUE, FALSE | - |
MIA | logical | FALSE | TRUE, FALSE | - |
nresample | integer | 9999 | \([1, \infty)\) | |
tol | numeric | 1.490116e-08 | \([0, \infty)\) | |
maxsurrogate | integer | 0 | \([0, \infty)\) | |
mtry | integer | - | \([0, \infty)\) | |
maxdepth | integer | - | \([0, \infty)\) | |
multiway | logical | FALSE | TRUE, FALSE | - |
splittry | integer | 2 | \([1, \infty)\) | |
intersplit | logical | FALSE | TRUE, FALSE | - |
majority | logical | FALSE | TRUE, FALSE | - |
caseweights | logical | TRUE | TRUE, FALSE | - |
sigma | numeric | 0.1 | \([0, 1]\) | |
ipcw | untyped | 1 | - | |
na.action | untyped | :: , 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
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvBlackBoost
Examples
learner = mlr3::lrn("surv.blackboost")
print(learner)
#> <LearnerSurvBlackBoost:surv.blackboost>: Gradient Boosting
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
#> * Predict Types: crank, [distr], lp
#> * 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"