mlr_learners_surv.xgboost: Extreme Gradient Boosting Survival Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Extreme Gradient Boosting Survival Learner

Description

eXtreme Gradient Boosting regression. Calls xgboost::xgb.train() from package xgboost.

Note: We strongly advise to use the separate Cox and AFT xgboost survival learners since they represent two very distinct survival modeling methods and we offer more prediction types in the respective learners compared to the ones available here. This learner will be deprecated in the future.

Initial parameter values

• nrounds is initialized to 1.

• nthread is initialized to 1 to avoid conflicts with parallelization via future.

• verbose is initialized to 0.

• objective is initialized to survival:cox for survival analysis.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("surv.xgboost")
lrn("surv.xgboost")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “lp”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, xgboost

Parameters

Id	Type	Default	Levels	Range
aft_loss_distribution	character	normal	normal, logistic, extreme	-
aft_loss_distribution_scale	numeric	-		(-\infty, \infty)
alpha	numeric	0		[0, \infty)
base_score	numeric	0.5		(-\infty, \infty)
booster	character	gbtree	gbtree, gblinear, dart	-
callbacks	untyped	list()		-
colsample_bylevel	numeric	1		[0, 1]
colsample_bynode	numeric	1		[0, 1]
colsample_bytree	numeric	1		[0, 1]
disable_default_eval_metric	logical	FALSE	TRUE, FALSE	-
early_stopping_rounds	integer	NULL		[1, \infty)
early_stopping_set	character	none	none, train, test	-
eta	numeric	0.3		[0, 1]
feature_selector	character	cyclic	cyclic, shuffle, random, greedy, thrifty	-
feval	untyped	NULL		-
gamma	numeric	0		[0, \infty)
grow_policy	character	depthwise	depthwise, lossguide	-
interaction_constraints	untyped	-		-
iterationrange	untyped	-		-
lambda	numeric	1		[0, \infty)
lambda_bias	numeric	0		[0, \infty)
max_bin	integer	256		[2, \infty)
max_delta_step	numeric	0		[0, \infty)
max_depth	integer	6		[0, \infty)
max_leaves	integer	0		[0, \infty)
maximize	logical	NULL	TRUE, FALSE	-
min_child_weight	numeric	1		[0, \infty)
missing	numeric	NA		(-\infty, \infty)
monotone_constraints	integer	0		[-1, 1]
normalize_type	character	tree	tree, forest	-
nrounds	integer	-		[1, \infty)
nthread	integer	1		[1, \infty)
num_parallel_tree	integer	1		[1, \infty)
objective	character	survival:cox	survival:cox, survival:aft	-
one_drop	logical	FALSE	TRUE, FALSE	-
print_every_n	integer	1		[1, \infty)
process_type	character	default	default, update	-
rate_drop	numeric	0		[0, 1]
refresh_leaf	logical	TRUE	TRUE, FALSE	-
sampling_method	character	uniform	uniform, gradient_based	-
sample_type	character	uniform	uniform, weighted	-
save_name	untyped	-		-
save_period	integer	-		[0, \infty)
scale_pos_weight	numeric	1		(-\infty, \infty)
seed_per_iteration	logical	FALSE	TRUE, FALSE	-
skip_drop	numeric	0		[0, 1]
strict_shape	logical	FALSE	TRUE, FALSE	-
subsample	numeric	1		[0, 1]
top_k	integer	0		[0, \infty)
tree_method	character	auto	auto, exact, approx, hist, gpu_hist	-
tweedie_variance_power	numeric	1.5		[1, 2]
updater	untyped	-		-
verbose	integer	1		[0, 2]
watchlist	untyped	NULL		-
xgb_model	untyped	-		-
device	untyped	-		-

Early stopping

Early stopping can be used to find the optimal number of boosting rounds. The early_stopping_set parameter controls which set is used to monitor the performance. By default, early_stopping_set = "none" which disables early stopping. Set early_stopping_set = "test" to monitor the performance of the model on the test set while training. The test set for early stopping can be set with the "test" row role in the mlr3::Task. Additionally, the range must be set in which the performance must increase with early_stopping_rounds and the maximum number of boosting rounds with nrounds. While resampling, the test set is automatically applied from the mlr3::Resampling. Not that using the test set for early stopping can potentially bias the performance scores.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvXgboost

Methods

Public methods

• LearnerSurvXgboost$new()

• LearnerSurvXgboost$importance()

• LearnerSurvXgboost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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

LearnerSurvXgboost$new()

---

Method importance()

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerSurvXgboost$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

Author(s)

be-marc

References

Chen, Tianqi, Guestrin, Carlos (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 785–794. ACM. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1145/2939672.2939785")}.

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

learner = mlr3::lrn("surv.xgboost")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.