mlr_learners_surv.glmboost: Boosted Generalized Linear Survival Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Boosted Generalized Linear Survival Learner

Description

Fits a generalized linear survival model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.glmboost")

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)		-
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	-		-

Prediction types

This learner returns two to three prediction types:

1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.glmboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.

2. crank: same as lp.

3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

Offset

If a Task contains a column with the offset role, it is automatically incorporated via the offset argument in mboost's training function. No offset is applied during prediction for this learner.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGLMBoost

Methods

Public methods

• LearnerSurvGLMBoost$new()

• LearnerSurvGLMBoost$importance()

• LearnerSurvGLMBoost$selected_features()

• LearnerSurvGLMBoost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvGLMBoost$new()

---

Method importance()

Importance scores are extracted with the function mboost::varimp() and represent a feature's individual contribution to the risk reduction per boosting step of the fitted model. The higher the risk reduction, the larger the feature importance.

Note: Importance is supported only for datasets with numeric features, as the presence of factors with multiple levels makes it difficult to get the original feature names.

Usage

LearnerSurvGLMBoost$importance()

Returns

Named numeric().

---

Method selected_features()

Selected features are extracted with the function mboost::coef.glmboost() which by default returns features with non-zero coefficients and for the final number of boosting iterations.

Note: Selected features can be retrieved only for datasets with numeric features, as the presence of factors with multiple levels makes it difficult to get the original feature names.

Usage

LearnerSurvGLMBoost$selected_features()

Returns

character().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvGLMBoost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

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

• 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.

Examples

# Define the Learner
learner = mlr3::lrn("surv.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on June 11, 2025, 7:06 p.m.