mlr_learners_surv.glmboost | R Documentation |
Fits a generalized linear survival model using a boosting algorithm.
Calls mboost::glmboost()
from mboost.
distr
prediction made by mboost::survFit()
.
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("surv.glmboost") lrn("surv.glmboost")
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
Id | Type | Default | Levels | Range |
offset | numeric | - | (-\infty, \infty) |
|
family | character | coxph | coxph, weibull, loglog, lognormal, gehan, cindex, custom | - |
custom.family | untyped | - | - | |
nuirange | untyped | c(0, 100) | - | |
center | logical | TRUE | TRUE, FALSE | - |
mstop | integer | 100 | [0, \infty) |
|
nu | numeric | 0.1 | [0, 1] |
|
risk | character | inbag | inbag, oobag, none | - |
oobweights | untyped | NULL | - | |
stopintern | logical | FALSE | TRUE, FALSE | - |
trace | logical | FALSE | TRUE, FALSE | - |
sigma | numeric | 0.1 | [0, 1] |
|
ipcw | untyped | 1 | - | |
na.action | untyped | stats::na.omit | - | |
contrasts.arg | untyped | - | - | |
mlr3::Learner
-> mlr3proba::LearnerSurv
-> LearnerSurvGLMBoost
new()
Creates a new instance of this R6 class. Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.
Importance is supported but fails tests as internally data is coerced to model matrix and original names can't be recovered.
description
Selected features are extracted with the function
mboost::variable.names.mboost()
, with
used.only = TRUE
.
return character()
.
LearnerSurvGLMBoost$new()
clone()
The objects of this class are cloneable with this method.
LearnerSurvGLMBoost$clone(deep = FALSE)
deep
Whether to make a deep clone.
RaphaelS1
Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.
Dictionary of Learners: mlr3::mlr_learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
learner = mlr3::lrn("surv.glmboost")
print(learner)
# available parameters:
learner$param_set$ids()
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