Extreme Gradient Boosting Regression Learner

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

We changed the following defaults for this learner:

• Verbosity is reduced by setting verbose to 0.

• Number of boosting iterations nrounds is set to 1.

Dictionary

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

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

References

Chen T, Guestrin C (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 -> mlr3::LearnerRegr -> LearnerRegrXgboost

Methods

Public methods

• LearnerRegrXgboost$new()

• LearnerRegrXgboost$importance()

• LearnerRegrXgboost$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()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrXgboost$new()

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Method importance()

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

Usage

LearnerRegrXgboost$importance()

Returns

Named numeric().

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.xgboost")
print(learner)
#> <LearnerRegrXgboost:regr.xgboost>
#> * Model: -
#> * Parameters: nrounds=1, verbose=0
#> * Packages: xgboost
#> * Predict Type: response
#> * Feature types: logical, integer, numeric
#> * Properties: importance, missings, weights

# available parameters:
learner$param_set$ids()
#>  [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"