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Extreme Gradient Boosting Classification Learner

Source: R/LearnerClassifXgboost.R
mlr_learners_classif.xgboost.Rd

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

If not specified otherwise, the evaluation metric is set to the default "logloss" for binary classification problems and set to "mlogloss" for multiclass problems. This was necessary to silence a deprecation warning.

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.

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.

Dictionary

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

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

Meta Information

  • Task type: “classif”

  • Predict Types: “response”, “prob”

  • 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_metricuntyped--
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\([1, \infty)\)
objectiveuntypedbinary:logistic-
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-
seed_per_iterationlogicalFALSETRUE, FALSE-
sampling_methodcharacteruniformuniform, gradient_based-
sample_typecharacteruniformuniform, weighted-
save_nameuntyped-
save_periodintegerNULL\([0, \infty)\)
scale_pos_weightnumeric1\((-\infty, \infty)\)
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-

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_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, mlr_learners_regr.xgboost

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifXgboost

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifXgboost$new()

Method importance()

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

Usage

LearnerClassifXgboost$importance()

Returns

Named numeric().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
learner$param_set$ids()
}
#> <LearnerClassifXgboost:classif.xgboost>
#> * Model: -
#> * Parameters: nrounds=1, nthread=1, verbose=0
#> * Packages: mlr3, mlr3learners, xgboost
#> * Predict Types:  [response], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: hotstart_forward, importance, missings, multiclass,
#>   twoclass, 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"                     "seed_per_iteration"         
#> [47] "sampling_method"             "sample_type"                
#> [49] "save_name"                   "save_period"                
#> [51] "scale_pos_weight"            "skip_drop"                  
#> [53] "strict_shape"                "subsample"                  
#> [55] "top_k"                       "training"                   
#> [57] "tree_method"                 "tweedie_variance_power"     
#> [59] "updater"                     "verbose"                    
#> [61] "watchlist"                   "xgb_model"