Skip to contents

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

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

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")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3learners, xgboost

Parameters

IdTypeDefaultLevelsRange
alphanumeric0\([0, \infty)\)
approxcontriblogicalFALSETRUE, FALSE-
base_scorenumeric0.5\((-\infty, \infty)\)
boostercharactergbtreegbtree, gblinear, dart-
callbacksuntypedlist-
colsample_bylevelnumeric1\([0, 1]\)
colsample_bynodenumeric1\([0, 1]\)
colsample_bytreenumeric1\([0, 1]\)
disable_default_eval_metriclogicalFALSETRUE, FALSE-
early_stopping_roundsintegerNULL\([1, \infty)\)
etanumeric0.3\([0, 1]\)
eval_metricuntypedrmse-
feature_selectorcharactercycliccyclic, shuffle, random, greedy, thrifty-
fevaluntyped-
gammanumeric0\([0, \infty)\)
grow_policycharacterdepthwisedepthwise, lossguide-
interaction_constraintsuntyped--
iterationrangeuntyped--
lambdanumeric1\([0, \infty)\)
lambda_biasnumeric0\([0, \infty)\)
max_bininteger256\([2, \infty)\)
max_delta_stepnumeric0\([0, \infty)\)
max_depthinteger6\([0, \infty)\)
max_leavesinteger0\([0, \infty)\)
maximizelogicalNULLTRUE, FALSE-
min_child_weightnumeric1\([0, \infty)\)
missingnumericNA\((-\infty, \infty)\)
monotone_constraintsuntyped0-
normalize_typecharactertreetree, forest-
nroundsinteger-\([1, \infty)\)
nthreadinteger1\([1, \infty)\)
ntreelimitintegerNULL\([1, \infty)\)
num_parallel_treeinteger1\([1, \infty)\)
objectiveuntypedreg:squarederror-
one_droplogicalFALSETRUE, FALSE-
outputmarginlogicalFALSETRUE, FALSE-
predcontriblogicalFALSETRUE, FALSE-
predictorcharactercpu_predictorcpu_predictor, gpu_predictor-
predinteractionlogicalFALSETRUE, FALSE-
predleaflogicalFALSETRUE, FALSE-
print_every_ninteger1\([1, \infty)\)
process_typecharacterdefaultdefault, update-
rate_dropnumeric0\([0, 1]\)
refresh_leaflogicalTRUETRUE, FALSE-
reshapelogicalFALSETRUE, FALSE-
sampling_methodcharacteruniformuniform, gradient_based-
sample_typecharacteruniformuniform, weighted-
save_nameuntyped-
save_periodintegerNULL\([0, \infty)\)
scale_pos_weightnumeric1\((-\infty, \infty)\)
seed_per_iterationlogicalFALSETRUE, FALSE-
skip_dropnumeric0\([0, 1]\)
strict_shapelogicalFALSETRUE, FALSE-
subsamplenumeric1\([0, 1]\)
top_kinteger0\([0, \infty)\)
traininglogicalFALSETRUE, FALSE-
tree_methodcharacterautoauto, exact, approx, hist, gpu_hist-
tweedie_variance_powernumeric1.5\([1, 2]\)
updateruntyped--
verboseinteger1\([0, 2]\)
watchlistuntyped-
xgb_modeluntyped-

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.

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

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.nnet, mlr_learners_regr.ranger, mlr_learners_regr.svm

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrXgboost

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method importance()

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

Usage

LearnerRegrXgboost$importance()

Returns

Named numeric().


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

if (requireNamespace("xgboost", quietly = TRUE)) {
  learner = mlr3::lrn("regr.xgboost")
  print(learner)

  # available parameters:
learner$param_set$ids()
}
#> <LearnerRegrXgboost:regr.xgboost>
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0
#> * Packages: mlr3, mlr3learners, xgboost
#> * Predict Types:  [response]
#> * Feature Types: logical, integer, numeric
#> * Properties: hotstart_forward, importance, missings, weights
#>  [1] "alpha"                       "approxcontrib"              
#>  [3] "base_score"                  "booster"                    
#>  [5] "callbacks"                   "colsample_bylevel"          
#>  [7] "colsample_bynode"            "colsample_bytree"           
#>  [9] "disable_default_eval_metric" "early_stopping_rounds"      
#> [11] "eta"                         "eval_metric"                
#> [13] "feature_selector"            "feval"                      
#> [15] "gamma"                       "grow_policy"                
#> [17] "interaction_constraints"     "iterationrange"             
#> [19] "lambda"                      "lambda_bias"                
#> [21] "max_bin"                     "max_delta_step"             
#> [23] "max_depth"                   "max_leaves"                 
#> [25] "maximize"                    "min_child_weight"           
#> [27] "missing"                     "monotone_constraints"       
#> [29] "normalize_type"              "nrounds"                    
#> [31] "nthread"                     "ntreelimit"                 
#> [33] "num_parallel_tree"           "objective"                  
#> [35] "one_drop"                    "outputmargin"               
#> [37] "predcontrib"                 "predictor"                  
#> [39] "predinteraction"             "predleaf"                   
#> [41] "print_every_n"               "process_type"               
#> [43] "rate_drop"                   "refresh_leaf"               
#> [45] "reshape"                     "sampling_method"            
#> [47] "sample_type"                 "save_name"                  
#> [49] "save_period"                 "scale_pos_weight"           
#> [51] "seed_per_iteration"          "skip_drop"                  
#> [53] "strict_shape"                "subsample"                  
#> [55] "top_k"                       "training"                   
#> [57] "tree_method"                 "tweedie_variance_power"     
#> [59] "updater"                     "verbose"                    
#> [61] "watchlist"                   "xgb_model"