mlr_learners_surv.gbm: Survival Gradient Boosting Machine Learner

mlr_learners_surv.gbmR Documentation

Survival Gradient Boosting Machine Learner

Description

Gradient Boosting for Survival Analysis. Calls gbm::gbm() from gbm.

Prediction types

This learner returns two prediction types, using the internal predict.gbm() function:

  1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation.

  2. crank: same as lp.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.gbm")

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “lp”

  • Feature Types: “integer”, “numeric”, “factor”, “ordered”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, gbm

Parameters

Id Type Default Levels Range
distribution character coxph coxph -
n.trees integer 100 [1, \infty)
cv.folds integer 0 [0, \infty)
interaction.depth integer 1 [1, \infty)
n.minobsinnode integer 10 [1, \infty)
shrinkage numeric 0.001 [0, \infty)
bag.fraction numeric 0.5 [0, 1]
train.fraction numeric 1 [0, 1]
keep.data logical FALSE TRUE, FALSE -
verbose logical FALSE TRUE, FALSE -
var.monotone untyped - -
n.cores integer 1 (-\infty, \infty)
single.tree logical FALSE TRUE, FALSE -

Initial parameter values

  • distribution:

  • Actual default: "bernoulli"

  • Adjusted default: "coxph"

  • Reason for change: This is the only distribution available for survival.

  • keep.data:

    • Actual default: TRUE

    • Adjusted default: FALSE

    • Reason for change: keep.data = FALSE saves memory during model fitting.

  • n.cores:

    • Actual default: NULL

    • Adjusted default: 1

    • Reason for change: Suppressing the automatic internal parallelization if cv.folds > 0 and avoid threading conflicts with future.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGBM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerSurvGBM$new()

Method importance()

The importance scores are extracted from the model slot variable.importance.

Usage
LearnerSurvGBM$importance()
Returns

Named numeric().


Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerSurvGBM$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.

See Also

Examples


# Define the Learner
learner = mlr3::lrn("surv.gbm")
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 Dec. 21, 2024, 2:21 p.m.