Boosted Generalized Additive Survival Learner
mlr_learners_surv.gamboost.Rd
Fits a generalized additive survival model using a boosting algorithm.
Calls mboost::gamboost()
from mboost.
Details
distr
prediction made by mboost::survFit()
.
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “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 | numeric | - | \((-\infty, \infty)\) | |
center | logical | TRUE | TRUE, FALSE | - |
mstop | integer | 100 | \([0, \infty)\) | |
nu | numeric | 0.1 | \([0, 1]\) | |
risk | character | inbag | inbag, oobag, none | - |
stopintern | untyped | FALSE | - | |
trace | logical | FALSE | TRUE, FALSE | - |
oobweights | untyped | NULL | - | |
baselearner | character | bbs | bbs, bols, btree | - |
dfbase | integer | 4 | \([0, \infty)\) | |
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
-> LearnerSurvGAMBoost
Methods
Method importance()
The importance scores are extracted with the function mboost::varimp()
with the default arguments.
Returns
Named numeric()
.
Method selected_features()
Selected features are extracted with the function
mboost::variable.names.mboost()
, with
used.only = TRUE
.
Examples
lrn("surv.gamboost")
#> <LearnerSurvGAMBoost:surv.gamboost>: Boosted Generalized Additive Model
#> * Model: -
#> * Parameters: family=coxph
#> * Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma
#> * Predict Types: crank, [distr], lp
#> * Feature Types: logical, integer, numeric, factor
#> * Properties: importance, selected_features, weights