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

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.

  • verbose:

    • Actual default: 1

    • Adjusted default: 0

    • Reason for change: Reduce verbosity.

  • objective:

    • Actual default: reg:squarederror

    • Adjusted default: survival:cox

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

  • eval_metric:

    • Actual default: no default

    • Adjusted default: cox-nloglik

    • Reason for change: Only sensible metric for 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")

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. doi: 10.1145/2939672.2939785 .

See also

Super classes

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

Methods

Public methods

Inherited 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.

Examples

if (requireNamespace("xgboost")) { learner = mlr3::lrn("surv.xgboost") print(learner) # available parameters: learner$param_set$ids() }
#> <LearnerSurvXgboost:surv.xgboost> #> * Model: - #> * Parameters: nrounds=1, verbose=0, eval_metric=cox-nloglik #> * Packages: xgboost #> * Predict Type: crank #> * Feature types: logical, integer, numeric #> * Properties: importance, missings, weights
#> [1] "booster" "watchlist" "eta" #> [4] "gamma" "max_depth" "min_child_weight" #> [7] "subsample" "colsample_bytree" "colsample_bylevel" #> [10] "colsample_bynode" "num_parallel_tree" "lambda" #> [13] "lambda_bias" "alpha" "objective" #> [16] "eval_metric" "base_score" "max_delta_step" #> [19] "missing" "monotone_constraints" "tweedie_variance_power" #> [22] "nthread" "nrounds" "feval" #> [25] "verbose" "print_every_n" "early_stopping_rounds" #> [28] "maximize" "sample_type" "normalize_type" #> [31] "rate_drop" "skip_drop" "one_drop" #> [34] "tree_method" "grow_policy" "max_leaves" #> [37] "max_bin" "callbacks" "sketch_eps" #> [40] "scale_pos_weight" "updater" "refresh_leaf" #> [43] "feature_selector" "top_k" "predictor"