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.

• 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")

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, nthread=1, verbose=0
#> * Packages: xgboost
#> * Predict Type: crank
#> * Feature types: integer, numeric
#> * Properties: importance, missings, weights
#>  [1] "booster"                     "watchlist"                  
#>  [3] "eta"                         "gamma"                      
#>  [5] "max_depth"                   "min_child_weight"           
#>  [7] "subsample"                   "colsample_bytree"           
#>  [9] "colsample_bylevel"           "colsample_bynode"           
#> [11] "num_parallel_tree"           "lambda"                     
#> [13] "lambda_bias"                 "alpha"                      
#> [15] "aft_loss_distribution"       "aft_loss_distribution_scale"
#> [17] "objective"                   "base_score"                 
#> [19] "max_delta_step"              "missing"                    
#> [21] "monotone_constraints"        "tweedie_variance_power"     
#> [23] "nthread"                     "nrounds"                    
#> [25] "feval"                       "verbose"                    
#> [27] "print_every_n"               "early_stopping_rounds"      
#> [29] "maximize"                    "save_period"                
#> [31] "save_name"                   "xgb_model"                  
#> [33] "sample_type"                 "normalize_type"             
#> [35] "rate_drop"                   "skip_drop"                  
#> [37] "one_drop"                    "tree_method"                
#> [39] "grow_policy"                 "max_leaves"                 
#> [41] "max_bin"                     "callbacks"                  
#> [43] "sketch_eps"                  "scale_pos_weight"           
#> [45] "updater"                     "refresh_leaf"               
#> [47] "feature_selector"            "top_k"                      
#> [49] "predictor"                   "ntreelimit"