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mlr3learners (version 0.5.1)

mlr_learners_surv.xgboost: Extreme Gradient Boosting Survival Learner

Description

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

Arguments

Custom mlr3 defaults

  • nrounds:

    • Actual default: no default.

    • Adjusted default: 1.

    • Reason for change: Without a default construction of the learner would error. Just setting a nonsense default to workaround this. nrounds needs to be tuned by the user.

  • nthread:

    • Actual value: Undefined, triggering auto-detection of the number of CPUs.

    • Adjusted value: 1.

    • Reason for change: Conflicting with parallelization via future.

  • verbose:

    • Actual default: 1.

    • Adjusted default: 0.

    • Reason for change: Reduce verbosity.

  • objective:

    • Actual default: reg:squarederror.

    • Adjusted default: survival:cox.

    • Reason for change: Changed to a survival objective.

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, mlr3learners, 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 list NULL -
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 \([1, \infty)\)
eta numeric 0.3 \([0, 1]\)
feature_selector character cyclic cyclic, shuffle, random, greedy, thrifty -
feval list NULL -
gamma numeric 0 \([0, \infty)\)
grow_policy character depthwise depthwise, lossguide -
interaction_constraints list - -
iterationrange list - -
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 \([1, \infty)\)
ntreelimit integer - \([1, \infty)\)
num_parallel_tree integer 1 \([1, \infty)\)
objective character survival:cox survival:cox, survival:aft -
one_drop logical FALSE TRUE, FALSE -
predictor character cpu_predictor cpu_predictor, gpu_predictor -
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 list - -
save_period integer - \([0, \infty)\)
scale_pos_weight numeric 1 \((-\infty, \infty)\)
seed_per_iteration logical FALSE TRUE, FALSE -
sketch_eps numeric 0.03 \([0, 1]\)
skip_drop numeric 0 \([0, 1]\)
single_precision_histogram logical FALSE TRUE, FALSE -
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 list - -
verbose integer 1 \([0, 2]\)
watchlist list NULL -
xgb_model list - -

Super classes

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

Methods

Public methods

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.

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. 10.1145/2939672.2939785.

See Also

  • Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

  • Package mlr3extralearners for more learners.

  • Dictionary of Learners: mlr_learners

  • as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

  • mlr3pipelines to combine learners with pre- and postprocessing steps.

  • Extension packages for additional task types:

    • mlr3proba for probabilistic supervised regression and survival analysis.

    • mlr3cluster for unsupervised clustering.

  • mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_classif.cv_glmnet, mlr_learners_classif.glmnet, mlr_learners_classif.kknn, mlr_learners_classif.lda, mlr_learners_classif.log_reg, mlr_learners_classif.multinom, mlr_learners_classif.naive_bayes, mlr_learners_classif.nnet, mlr_learners_classif.qda, mlr_learners_classif.ranger, mlr_learners_classif.svm, mlr_learners_classif.xgboost, mlr_learners_regr.cv_glmnet, mlr_learners_regr.glmnet, mlr_learners_regr.kknn, mlr_learners_regr.km, mlr_learners_regr.lm, mlr_learners_regr.ranger, mlr_learners_regr.svm, mlr_learners_regr.xgboost, mlr_learners_surv.cv_glmnet, mlr_learners_surv.glmnet, mlr_learners_surv.ranger

Examples

Run this code
# NOT RUN {
if (requireNamespace("xgboost", quietly = TRUE)) {
  learner = mlr3::lrn("surv.xgboost")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
# }

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