Extreme Gradient Boosting Survival Learner

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

Dictionary of Learners: mlr3::mlr_learners

Super classes

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

Methods

Public methods

• LearnerSurvXgboost$new()

• LearnerSurvXgboost$importance()

• LearnerSurvXgboost$clone()

Inherited methods

• 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.

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"